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Recurrence is a major cause of death among BRCA1/2 mutation carriers with breast (BrCa) and ovarian cancers (OvCa). We performed multi-omic sequencing on 67 paired primary and recurrent BrCa and OvCa from 27 BRCA1/2 mutation carriers to identify potential recurrence-specific drivers. PARP1 amplifications were identified in recurrences (FDR q = 0.05), and PARP1 was significantly overexpressed across primary BrCa and recurrent BrCa and OvCa, independent of amplification status. RNA-seq analysis found two BRCA2 isoforms, BRCA2-201/Long and BRCA2-001/Short, predicted sensitive and insensitive to nonsense-mediated decay, respectively. BRCA2-001/Short was expressed more frequently in recurrences and associated with reduced overall survival in breast cancer (87 vs. 121 months; HR = 2.5 [1.18–5.5]). Loss of heterozygosity (LOH) status was discordant in 25% of patient’s primary and recurrent tumors, with switching between both LOH and lack of LOH found. Our study revealed multiple potential drivers of recurrent disease in BRCA1/2 mutation-associated cancer, improving our understanding of tumor evolution and suggesting potential biomarkers.

Testicular germ cell tumors (TGCT) are the most common tumor in young white men and have a high heritability. In this study, the international Testicular Cancer Consortium assemble 10,156 and 179,683 men with and without TGCT, respectively, for a genome-wide association study. This meta-analysis identifies 22 TGCT susceptibility loci, bringing the total to 78, which account for 44% of disease heritability. Men with a polygenic risk score (PRS) in the 95th percentile have a 6.8-fold increased risk of TGCT compared to men with median scores. Among men with independent TGCT risk factors such as cryptorchidism, the PRS may guide screening decisions with the goal of reducing treatment-related complications causing long-term morbidity in survivors. These findings emphasize the interconnected nature of two known pathways that promote TGCT susceptibility: male germ cell development within its somatic niche and regulation of chromosomal division and structure, and implicate an additional biological pathway, mRNA translation., Testicular germ cell tumors are highly heritable, and the authors present the largest genome association study, identifying 22 novel loci, which account for a third of those identified to date. Implicated pathways include male germ cell development and differentiation, and chromosomal segregation.

[EN] Medial temporal lobe (MTL) substructures are the earliest regions affected by neurofibrillary tangle pathology-and thus are promising biomarkers for Alzheimer's disease (AD). However, automatic segmentation of the MTL using only T1-weighted (T1w) magnetic resonance imaging (MRI) is challenging due to the large anatomical variability of the MTL cortex and the confound of the dura mater, which is commonly segmented as gray matter by state-of-the-art algorithms because they have similar intensity in T1w MRI. To address these challenges, we developed a novel atlas set, consisting of 15 cognitively normal older adults and 14 patients with mild cognitive impairment with a label explicitly assigned to the dura, that can be used by the multiatlas automated pipeline (Automatic Segmentation of Hippocampal Subfields [ASHS-T1]) for the segmentation of MTL subregions, including anterior/posterior hippocampus, entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36, and parahippocampal cortex on T1w MRI. Cross-validation experiments indicated good segmentation accuracy of ASHS-T1 and that the dura can be reliably separated from the cortex (6.5% mislabeled as gray matter). Conversely, FreeSurfer segmented majority of the dura mater (62.4%) as gray matter and the degree of dura mislabeling decreased with increasing disease severity. To evaluate its clinical utility, we applied the pipeline to T1w images of 663 ADNI subjects and significant volume/thickness loss is observed in BA35, ERC, and posterior hippocampus in early prodromal AD and all subregions at later stages. As such, the publicly available new atlas and ASHS-T1 could have important utility in the early diagnosis and monitoring of AD and enhancing brain-behavior studies of these regions., Northern California Institute for Research and Education; Foundation for the National Institutes of Health; Canadian Institutes of Health Research; Transition Therapeutics; Takeda Pharmaceutical Company; Servier; Piramal Imaging; Pfizer Inc.; Novartis Pharmaceuticals Corporation; Neurotrack Technologies; NeuroRx Research; Meso Scale Diagnostics, LLC.; Lundbeck and Merck & Co., Inc.; Lumosity; Johnson & Johnson Pharmaceutical Research & Development LLC.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; IXICO Ltd.; GE Healthcare; Fujirebio; Genentech, Inc.; F. Hoffmann-La Roche Ltd.; EuroImmun; Eli Lilly and Company; Elan Pharmaceuticals, Inc.; Cogstate and Eisai Inc.; CereSpir, Inc.; Bristol-Myers Squibb Company; Biogen; BioClinica, Inc.; Araclon Biotech; Alzheimer's Drug Discovery Foundation; Alzheimer's Association; AbbVie; National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Department of Defense ADNI, Grant/Award Number: W81XWH-12-2-0012; Alzheimer's Disease Neuroimaging Initiative, Grant/Award Number: U01 AG024904; Spain Ministry of Economy, Industry and Competitiveness, Grant/Award Number: DPI2017-87743-R; Foundation Philippe Chatrier; BrightFocus Foundation; National Institutes of Health, Grant/Award Numbers: R01-AG055005, R01-EB017255, P30-AG010124, R01-AG040271, R01-AG056014

Background: Ipilimumab is an immune checkpoint inhibitor used to treat melanoma. Although the development of immune-related adverse events (irAEs) among patients treated with ipilimumab is well documented, little is known about factors that may increase risk of irAEs. We conducted a genome-wide association study (GWAS) to examine the genetic susceptibility to irAEs in response to ipilimumab monotherapy. Methods: In partnership with Bristol Myers Squibb BMS, extant genotype data and clinical information were obtained on melanoma patients treated with ipilimumab monotherapy from three clinical trials. Only patients who were treatment naïve were included in our analyses. We defined our outcome as the occurrence of a serious irAE, grade 3 or higher. We first analyzed data from 294 subjects, 79 with severe irAE, enrolled on CA184-169 for whom genotyping was completed using the Affymetrix 6 array. After appropriate quality control, SNP associations were determined using logistic regression models that were adjusted for ancestry, ECOG status, ipilimumab dosage (3 mg/kg vs 10 mg/kg), and number of doses ( Results: The most statistically significant marker (rs55981606, p=1.39 × 10-7) and a second independent marker (rs72712605, p=6.33 × 10-6) mapped to a non-coding region on chromosome 9. We identified a marker (rs65949485, p = 9.38 × 10-6) intragenic between the SHQ1 and GXYLT2 genes, both of which are involved in the Notch signaling pathway. Markers proximal to NR2F2 (rs13270533, p=7.8 × 10-6) and within SAMD12 (rs13270533, p = 9.23 × 10-6) were also identified; the latter two genes have been implicated as being oncogenic. Additionally, we identified several markers implicating genes involved in inflammation, specifically macrophage activation, including TLE1 (rs3739581, p = 9.25 × 10-7), SLC16A4 (rs2271885, p=9.23 × 10-6) and CYP2J2 (rs427970, p=2.03 × 10-6). Conclusions: Results from our meta-analysis suggest that genes related to inflammation processes and those with known contributions to oncogenesis may play a role in the development of severe irAEs resulting from ipilimumab monotherapy. If further validated, these findings may provide the foundation to advance models to discriminate patients with a high likelihood of suffering irAE allowing for heightened surveillance of symptom onset or joint decision making for alternative therapies. Citation Format: John Pluta, Lu Qian, Kurt D'Andrea, Chunzhe Duan, Benita Weathers, Megan Wind-Rotolo, Peter Kanetsky, Katherine Nathanson. Genetic susceptibility to immune-related adverse events among melanoma patients treated with ipilimumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1352.

Objectives To assess risk factors for cerebrospinal fluid (CSF) leak and complications after microvascular reconstruction of cranio-orbitofacial resection with orbital exenteration (CFOE). Study Design Retrospective case series. Methods Seventy consecutive patients at a tertiary hospital underwent 76 procedures with microvascular reconstruction of CFOE defects. Patients were stratified by extent of skull base exposure and presence or absence of dural resection. Patients with exposure of the orbital apex and roof alone were classified as minimal skull base exposure (MS) (n = 32). Those with skull base exposure beyond the orbital apex and roof were classified as significant skull base exposure (SS) (n = 38) and were subdivided into those with dural exposure (SSe) (n = 15) and those with dural resection (SSr) (n = 23). The main outcome measure was incidence of postoperative CSF leak according to univariate and multivariate analysis of risk factors, including previous radiation, surgery, and location of defect. Results Intraoperative leaks occurred in five and four patients in the MS and SSe groups, respectively, with no postoperative leaks. In the SSr group, five patients developed a postoperative CSF leak and three required operative management. Multivariate analysis revealed middle fossa exposure to be the only significant predictor of CSF leak (P = 0.03). The overall complication rate was 31.6%. Major complications were greater in the SS group compared to the MS group (P = 0.05). Conclusion In this series, middle fossa resection increased the risk of postoperative CSF leak after microvascular reconstruction of CFOE defects, and complication rates were greater with more complex defects. Level of Evidence 4. Laryngoscope, 127:835–841, 2017

Carriers of pathogenic germline mutations in BRCA1/2 are highly predisposed to breast and ovarian cancers. Although their primary tumors respond to DNA-damaging agents, such as platinum-based chemotherapy and poly (ADP-ribose) polymerase inhibitors (PARPi), these malignancies often return as therapy-resistant recurrences. To identify genetic mechanisms of therapeutic resistance, we performed whole exome sequencing on 41 pairs of matched primary/recurrent breast and ovarian tumors from 27 BRCA1/2 mutation carriers. The cohort consisted of 14 ovarian cancer patients (nine BRCA1, five BRCA2) and 13 breast cancer patients (nine BRCA1, four BRCA2). Five patients received PARPi, 17 patients received platinums, and all patients received some type of chemotherapy prior to recurrence. First, we performed a segmentation analysis to assess copy number variation (CNVs) in each tumor. We calculated homologous recombination deficiency (HRD) scores for each tumor, but found no significant differences between matched primary/recurrent pairs. We noted that primary tumors had a relatively high average HRD score (55/100), which remained high in recurrences (52/100). Next, we totaled arm-level CNVs to generate aneuploidy scores for each tumor. Using a Wilcoxon signed rank test, we found that aneuploidy scores were significantly higher in recurrences than in matched primary tumors (p=0.007). This finding suggests that recurrences have more arm-level CNVs than primary tumors, but that acquired genomic abnormalities are not caused by HRD per se. Instead, arm-level CNVs indicate that replication errors or genome doubling events are more common in recurrences than in primary tumors. Lastly, we annotated all tumors' CNVs with genes to interrogate potential effects on signaling pathways, including CNVs exclusive to each recurrence. Using Gene Set Enrichment Analysis with Hallmark gene sets, we identified pathways significantly (FDR q Citation Format: Jennifer Brady Shah, Bradley Wubbenhorst, John Pluta, Caitlin Feltcher, Lauren Schmucker, Kurt D'Andrea, Heather Symecko, Catherine Ruan, Anupma Nayak, Kara N. Maxwell, Susan Domchek, Katherine L. Nathanson. Copy number variation in recurrent BRCA1/2 germline mutation-associated breast and ovarian cancers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2500.

Although temporal lobe epilepsy (TLE) and resection (TLR) impact olfactory eloquent brain structures, their influences on olfaction remain enigmatic. We sought to more definitively assess the influences of TLE and TLR on olfaction using three well-validated olfactory tests and measuring the tests’ associations with the volume of numerous temporal lobe brain structures. The University of Pennsylvania Smell Identification Test and an odor detection threshold test were administered to 71 TLE patients and 71 age- and sex-matched controls; 69 TLE patients and controls received an odor discrimination/memory test. Fifty-seven patients and 57 controls were tested on odor identification and threshold before and after TLR; 27 patients and 27 controls were similarly tested for odor detection/discrimination. Scores were compared using analysis of variance and correlated with pre- and post-operative volumes of the target brain structures. TLE was associated with bilateral deficits in all test measures. TLR further decreased function on the side ipsilateral to resection. The hippocampus and other structures were smaller on the focus side of the TLE subjects. Although post-operative volumetric decreases were evident in most measured brain structures, modest contralateral volumetric increases were observed in some cases. No meaningful correlations were evident pre- or post-operatively between the olfactory test scores and the structural volumes. In conclusion, we demonstrate that smell dysfunction is clearly a key element of both TLE and TLR, impacting odor identification, detection, and discrimination/memory. Whether our novel finding of significant post-operative increases in the volume of brain structures contralateral to the resection side reflects plasticity and compensatory processes requires further study.

Although the hippocampus is one of the most studied structures in the human brain, limited quantitative data exist on its 3D organization, anatomical variability, and effects of disease on its subregions. Histological studies provide restricted reference information due to their 2D nature. In this paper, high-resolution (∼200 × 200 × 200 μm3) ex vivo MRI scans of 31 human hippocampal specimens are combined using a groupwise diffeomorphic registration approach into a 3D probabilistic atlas that captures average anatomy and anatomic variability of hippocampal subfields. Serial histological imaging in 9 of the 31 specimens was used to label hippocampal subfields in the atlas based on cytoarchitecture. Specimens were obtained from autopsies in patients with a clinical diagnosis of Alzheimer's disease (AD; 9 subjects, 13 hemispheres), of other dementia (nine subjects, nine hemispheres), and in subjects without dementia (seven subjects, nine hemispheres), and morphometric analysis was performed in atlas space to measure effects of age and AD on hippocampal subfields. Disproportional involvement of the cornu ammonis (CA) 1 subfield and stratum radiatum lacunosum moleculare was found in AD, with lesser involvement of the dentate gyrus and CA2/3 subfields. An association with age was found for the dentate gyrus and, to a lesser extent, for CA1. Three-dimensional patterns of variability and disease and aging effects discovered via the ex vivo hippocampus atlas provide information highly relevant to the active field of in vivo hippocampal subfield imaging.

BACKGROUND AND PURPOSE: High resolution 7T MRI is increasingly used to investigate hippocampal subfields in vivo, but most studies rely on manual segmentation which is labor intensive. We aimed to evaluate an automated technique to segment hippocampal subfields and the entorhinal cortex at 7T MRI. MATERIALS AND METHODS: The cornu ammonis (CA)1, CA2, CA3, dentate gyrus, subiculum, and entorhinal cortex were manually segmented, covering most of the long axis of the hippocampus on 0.70-mm 3 T2-weighted 7T images of 26 participants (59 ± 9 years, 46% men). The automated segmentation of hippocampal subfields approach was applied and evaluated by using leave-one-out cross-validation. RESULTS: Comparison of automated segmentations with corresponding manual segmentations yielded a Dice similarity coefficient of >0.75 for CA1, the dentate gyrus, subiculum, and entorhinal cortex and >0.54 for CA2 and CA3. Intraclass correlation coefficients were >0.74 for CA1, the dentate gyrus, and subiculum; and >0.43 for CA2, CA3, and the entorhinal cortex. Restricting the comparison of the entorhinal cortex segmentation to a smaller range along the anteroposterior axis improved both intraclass correlation coefficients (left: 0.71; right: 0.82) and Dice similarity coefficients (left: 0.78; right: 0.77). The accuracy of the automated segmentation versus a manual rater was lower, though only slightly for most subfields, than the intrarater reliability of an expert manual rater, but it was similar to or slightly higher than the accuracy of an expert-versus-manual rater with ∼170 hours of training for almost all subfields. CONCLUSIONS: This work demonstrates the feasibility of using a computational technique to automatically label hippocampal subfields and the entorhinal cortex at 7T MRI, with a high accuracy for most subfields that is competitive with the labor-intensive manual segmentation. The software and atlas are publicly available: http://www.nitrc.org/projects/ashs/.

PURPOSE Phacoemulsification has been cited as a possible cause of bleb failure in eyes with prior trabeculectomy. No method has been developed to directly evaluate the risk of bleb failure after phacoemulsification. We investigate the use of trypan blue during cataract surgery in the setting of a preexisting trabeculectomy to evaluate the functional status of the bleb and predict postoperative bleb function. MATERIALS AND METHODS In total, 14 patients contributing 1 eye each with a history of prior trabeculectomy with mitomycin C undergoing phacoemulsification with intraocular lens implantation were enrolled in this prospective, nonrandomized clinical trial. At the time of phacoemulsification, trypan blue was instilled into the anterior chamber before capsulorhexis creation. Staining of the bleb was grouped as being mild or diffuse using intraoperative photographs. These eyes were followed for 1 year postoperatively and evaluated for intraocular pressure (IOP) control. RESULTS The change in IOP was not significantly different between the 2 groups (P=0.14). A trend towards greater need for IOP-lowering medications was noted (P

Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules is challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200 μm spacing and 5 μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4 Tesla MRI scan of the intact, whole hippocampal formation acquired with 160 μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1 cm-thick tissue sub-blocks acquired with 200 μm isotropic resolution. These 1 cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.

Multi-atlas segmentation is an effective approach for automatically labeling objects of interest in biomedical images. In this approach, multiple expert-segmented example images, called atlases, are registered to a target image, and deformed atlas segmentations are combined using label fusion. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity have been particularly successful. However, one limitation of these strategies is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this limitation, we propose a new solution for the label fusion problem, in which weighted voting is formulated in terms of minimizing the total expectation of labeling error, and in which pairwise dependency between atlases is explicitly modeled as the joint probability of two atlases making a segmentation error at a voxel. This probability is approximated using intensity similarity between a pair of atlases and the target image in the neighborhood of each voxel. We validate our method in two medical image segmentation problems: hippocampus segmentation and hippocampus subfield segmentation in magnetic resonance (MR) images. For both problems, we show consistent and significant improvement over label fusion strategies that assign atlas weights independently.

Automatic segmentation of cortical and subcortical structures is commonplace in brain MRI literature and is frequently used as the first step towards quantitative analysis of structural and functional neuroimaging. Most approaches to brain structure segmentation are based on propagation of anatomical information from example MRI datasets, called atlases or templates, that are manually labeled by experts. The accuracy of automatic segmentation is usually validated against the “bronze” standard of manual segmentation of test MRI datasets. However, good performance vis-a-vis manual segmentation does not imply accuracy relative to the underlying true anatomical boundaries. In the context of segmentation of hippocampal subfields and functionally related medial temporal lobe cortical subregions, we explore the challenges associated with validating existing automatic segmentation techniques against underlying histologically-derived anatomical “gold” standard; and, further, developing automatic in vivo MRI segmentation techniques informed by histological imaging.

Objectives: To assess risk factors for cerebrospinal fluid (CSF) leak after microvascular reconstruction of extensive cranio-orbitofacial resection with orbital exenteration (CFOE). Study Design: Retrospective Case Series Methods: 70 consecutive patients at a tertiary hospital underwent 76 procedures with microvascular reconstruction of CFOE defects. Patients were stratified by extent of skull base exposure and presence or absence of dural resection. Patients with exposure of the orbital apex and roof alone were classified as minimal skullbase exposure (MSE, n=32). Those with exposure beyond the orbital apex and roof were classified as significant skullbase exposure (SSE, n=38), including those with dural resection (n=23). The main outcome measure was incidence of postoperative CSF leak according to univariate and multivariate analysis of risk factors. Results: Five patients developed a postoperative CSF leak, and 3 required operative management. All 5 were SSE with dural resection and had middle fossa exposure, previous radiation and 4 had previous surgery. None of the MSE group or SSE without dural resection or SSE with anterior fossa exposure alone developed a CSF leak. Multivariate analysis revealed middle fossa exposure to be the only significant predictor of CSF leak (p=0.03). The overall complication rate was 31.6%. Major complications were greater in the SSE group (p=0.05). Conclusion: Middle fossa exposure increases the risk of CSF leak in microvascular reconstruction of CFOE defects.

The measurement of hippocampal volumes using MRI is a useful in-vivo biomarker for detection and monitoring of early Alzheimer's disease (AD), including during the amnestic mild cognitive impairment (a-MCI) stage. The pathology underlying AD has regionally selective effects within the hippocampus. As such, we predict that hippocampal subfields are more sensitive in discriminating prodromal AD (i.e., a-MCI) from cognitively normal controls than whole hippocampal volumes, and attempt to demonstrate this using a semi-automatic method that can accurately segment hippocampal subfields. High-resolution coronal-oblique T2-weighted images of the hippocampal formation were acquired in 45 subjects (28 controls and 17 a-MCI (mean age: 69.5 ± 9.2; 70.2 ± 7.6)). CA1, CA2, CA3, and CA4/DG subfields, along with head and tail regions, were segmented using an automatic algorithm. CA1 and CA4/DG segmentations were manually edited. Whole hippocampal volumes were obtained from the subjects' T1-weighted anatomical images. Automatic segmentation produced significant group differences in the following subfields: CA1 (left: p = 0.001, right: p = 0.038), CA4/DG (left: p = 0.002, right: p = 0.043), head (left: p = 0.018, right: p = 0.002), and tail (left: p = 0.019). After manual correction, differences were increased in CA1 (left: p0.001, right: p = 0.002), and reduced in CA4/DG (left: p = 0.029, right: p = 0.221). Whole hippocampal volumes significantly differed bilaterally (left: p = 0.028, right: p = 0.009). This pattern of atrophy in a-MCI is consistent with the topography of AD pathology observed in postmortem studies, and corrected left CA1 provided stronger discrimination than whole hippocampal volume (p = 0.03). These results suggest that semi-automatic segmentation of hippocampal subfields is efficient and may provide additional sensitivity beyond whole hippocampal volumes.

Spatiotemporal data coherence widely exists in fMRI data and can be potentially used to increase brain activation detection. To assess this possibility, a two-stage fMRI data analysis method was presented in this work. Standard voxelwise general linear model (GLM) was used to first exclude voxels with low correlation to the functional design, and an intervoxel coherence map (ICM) was then calculated voxel by voxel to locate brain regions, which were most consistently activated during the functional experiment. Population inference about the detected effects was provided through random effect analysis or permutation testing. Evaluations using synthetic data and scene-encoding memory task fMRI data both showed enhanced activation detection performance for the proposed method when compared with standard GLM. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 33–36, 2012 © 2012 Wiley Periodicals, Inc.

Measurement of brain change due to neurodegenerative disease and treatment is one of the fundamental tasks of neuroimaging. Deformation-based morphometry (DBM) has been long recognized as an effective and sensitive tool for estimating the change in the volume of brain regions over time. This paper demonstrates that a straightforward application of DBM to estimate the change in the volume of the hippocampus can result in substantial bias, i.e., an overestimation of the rate of change in hippocampal volume. In ADNI data, this bias is manifested as a non-zero intercept of the regression line fitted to the 6 and 12 month rates of hippocampal atrophy. The bias is further confirmed by applying DBM to repeat scans of subjects acquired on the same day. This bias appears to be the result of asymmetry in the interpolation of baseline and followup images during longitudinal image registration. Correcting this asymmetry leads to bias-free atrophy estimation.

We evaluate the impact of template choice on template-based segmentation of the hippocampus in epilepsy. Four dataset-specific strategies are quantitatively contrasted: the “closest to average” individual template, the average shape version of the closest to average template, a best appearance template and the best appearance and shape template proposed here and implemented in the open source toolkit Advanced Normalization Tools (ANTS). The cross-correlation similarity metric drives the correspondence model and is used consistently to determine the optimal appearance. Minimum shape distance in the diffeomorphic space determines optimal shape. Our evaluation results show that, with respect to gold-standard manual labeling of hippocampi in epilepsy, optimal shape and appearance template construction outperforms the other strategies for gaining data-derived templates. Our results also show the improvement is most significant on the diseased side and insignificant on the healthy side. Thus, the importance of the template increases when used to study pathology and may be less critical for normal control studies. Furthermore, explicit geometric optimization of the shape component of the unbiased template positively impacts the study of diseased hippocampi.

Diffusion tensor (DT) and T1 structural magnetic resonance images provide unique and complementary tools for quantifying the living brain. We leverage both modalities in a diffeomorphic normalization method that unifies analysis of clinical datasets in a consistent and inherently multivariate (MV) statistical framework. We use this technique to study MV effects of traumatic brain injury (TBI).We contrast T1 and DT image-based measurements in the thalamus and hippocampus of 12 TBI survivors and nine matched controls normalized to a combined DT and T1 template space. The normalization method uses maps that are topology-preserving and unbiased. Normalization is based on the full tensor of information at each voxel and, simultaneously, the similarity between high-resolution features derived from T1 data. The technique is termed symmetric normalization for MV neuroanatomy (SyNMN). Voxel-wise MV statistics on the local volume and mean diffusion are assessed with Hotelling's T(2) test with correction for multiple comparisons.TBI significantly (false discovery rate P.05) reduces volume and increases mean diffusion at coincident locations in the mediodorsal thalamus and anterior hippocampus.SyNMN reveals evidence that TBI compromises the limbic system. This TBI morphometry study and an additional performance evaluation contrasting SyNMN with other methods suggest that the DT component may aid normalization quality.

The human hippocampus is a complex structure consisting of multiple anatomically and functionally distinct subfields. Obtaining subfield-specific measures from in vivo MRI is challenging, and can benefit from a detailed 3D anatomical reference. This paper builds a computational atlas of the hippocampus from high-resolution ex vivo MRI of 26 specimens using groupwise deformable registration. A surface-based approach based on the explicit segmentation and geometric modeling of hippocampal layers is used to initialize deformable registration of ex vivo MRI scans. This initialization improves of groupwise registration quality, as measured in terms of similarity metrics and qualitatively. The resulting atlas, which also includes annotations mapped from histology, is a unique resource for describing variability in hippocampal anatomy.

Multiple techniques for quantification of hippocampal subfields from in vivo MRI have been proposed. Linking in vivo MRI to the underlying histology can help validate and improve these techniques. High-resolution ex vivo MRI can provide an intermediate modality to map information between these very different imaging modalities. This article evaluates the ability to match information between in vivo and ex vivo MRI in the same subjects. We perform rigid and deformable registration on 10 pairs of in vivo (3 T, 0.4 × 0.4 × 2.6 mm3) and ex vivo (9.4 T, 0.2 × 0.2 × 0.2 mm3) scans, and describe differences in MRI appearance between these modalities qualitatively and quantitatively. The feasibility of using this dataset to validate in vivo segmentation is evaluated by applying an automatic hippocampal subfield segmentation technique (ASHS) to in vivo scans and comparing SRLM (stratum/radiatum/lacunosum/moleculare) surface to manual tracing on corresponding ex vivo scans (and in 2 cases, histology). Regional increases in thickness are detected in ex vivo scans adjacent to the ventricles and were not related to scanner, resolution differences, or susceptibility artefacts. Satisfactory in vivo/ex vivo registration and subvoxel accuracy of ASHS segmentation of hippocampal SRLM demonstrate the feasibility of using this dataset for validation, and potentially, improvement of in vivo segmentation methods.

The entorhinal cortex (ERC) and the perirhinal cortex (PRC) are subregions of the medial temporal lobe (MTL) that play important roles in episodic memory representations, as well as serving as a conduit between other neocortical areas and the hippocampus. They are also the sites where neuronal damage first occurs in Alzheimer's disease (AD). The ability to automatically quantify the volume and thickness of the ERC and PRC is desirable because these localized measures can potentially serve as better imaging biomarkers for AD and other neurodegenerative diseases. However, large anatomical variation in the PRC makes it a challenging area for analysis. In order to address this problem, we propose an automatic segmentation, clustering, and thickness measurement approach that explicitly accounts for anatomical variation. The approach is targeted to highly anisotropic (0.4x0.4x2.0mm3 ) T2-weighted MRI scans that are preferred by many authors for detailed imaging of the MTL, but which pose challenges for segmentation and shape analysis. After automatically labeling MTL substructures using multi-atlas segmentation, our method clusters subjects into groups based on the shape of the PRC, constructs unbiased population templates for each group, and uses the smooth surface representations obtained during template construction to extract regional thickness measurements in the space of each subject. The proposed thickness measures are evaluated in the context of discrimination between patients with Mild Cognitive Impairment (MCI) and normal controls (NC).

Objectives/Hypothesis Sentinel lymph node biopsy (SLNB) has been utilized for cutaneous melanoma and other malignancies arising from the eye and ocular adnexa. Currently, SLNB requires blue dyes and/or radiopharmaceuticals; both of which have significant shortcomings. This study sought to evaluate the feasibility of SLNB with the use of real-time, contrast-enhanced ultrasound (CEUS) as an alternative technique for tumors arising in the conjunctiva. Study Design Prospective feasibility study in a porcine model. Methods Twelve experiments were performed on six non–tumor-bearing Yorkshire swine. An ultrasound contrast agent, Sonazoid (GE Healthcare, Oslo, Norway), 99mtechnetium (99mTc), and methylene blue (MB) (Covidien, Mansfield, MA) were injected in the ocular conjunctiva. Sentinel lymph nodes (SLNs) were localized with CEUS and findings were compared to that of MB and 99mTc. Fisher exact test was used. Results Contrast-enhanced SLNs were identified within an average of 6.2 minutes from time of injection of Sonazoid. A total of 17 SLNs were identified by at least one of the three techniques. Correlation between Sonazoid and 99mTc was 94.1% (16/17 SLNs). Correlation between 99mTc and MB was 88.2% (15/17). One SLN that was positive for 99mTc but negative for Sonazoid and was considered to be a false positive (1/17); findings were similar for MB (1/17). Differences between the three techniques were not significant (P = .886). Conclusions CEUS-guided injection of conjunctiva for SLNB is technically feasible and correlates well with standard detection techniques. This technique shows promise for rapid, real-time, intraoperative imaging for SLNB, using a widely available imaging modality and avoiding the need for radiopharmaceuticals. Level of Evidence NA Laryngoscope, 124:2531–2536, 2014

Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by registering the image to a set of atlases [19, 11, 8]. Effective label fusion strategies, such as local similarity-weighted voting [1, 13] substantially reduce segmentation errors compared to single-atlas segmentation. This paper extends the label fusion idea to the problem of finding correspondences across a set of images. Instead of computing a consensus segmentation, weighted voting is used to estimate a consensus coordinate map between a target image and a reference space. Two variants of the problem are considered: (1) where correspondences between a set of atlases are known and are propagated to the target image; (2) where correspondences are estimated across a set of images without prior knowledge. Evaluation in synthetic data shows that correspondences recovered by fusion methods are more accurate than those based on registration to a population template. In a 2D example in real MRI data, fusion methods result in more consistent mappings between manual segmentations of the hippocampus.

Automatic segmentation using multi-atlas label fusion has been widely applied in medical image analysis. To simplify the label fusion problem, most methods implicitly make a strong assumption that the segmentation errors produced by different atlases are uncorrelated. We show that violating this assumption significantly reduces the efficiency of multi-atlas segmentation. To address this problem, we propose a regression-based approach for label fusion. Our experiments on segmenting the hippocampus in magnetic resonance images (MRI) show significant improvement over previous label fusion techniques.

The hippocampal formation (HF) is a brain structure of great interest because of its central role in learning and memory, and its associated vulnerability to several neurological disorders. In vivo oblique coronal T2-weighted MRI with high in-plane resolution (~0.5 mm × 0.5 mm), thick slices (~2.0 mm), and a field of view tailored to imaging the hippocampal formation (denoted HF-MRI in this paper) has been advanced as a useful imaging modality for detailed hippocampal morphometry. Cross-sectional analysis of volume measurements derived from HF-MRI has shown the modality's promise to yield sensitive imaging-based biomarker for neurological disorders such as Alzheimer's disease. However, the utility of this modality for making measurements of longitudinal change has not yet been demonstrated. In this paper, using an unbiased deformation-based morphometry (DBM) pipeline, we examine the suitability of HF-MRI for estimating longitudinal change by comparing atrophy rates measured in the whole hippocampus from this modality with those measured from more common isotropic (~1 mm³) T1-weighted MRI in the same set of individuals, in a cohort of healthy controls and patients with cognitive impairment. While measurements obtained from HF-MRI were largely consistent with those obtained from T1-MRI, HF-MRI yielded slightly larger group effect of greater atrophy rates in patients than in controls. The estimated minimum sample size required for detecting a 25% change in patients' atrophy rate in the hippocampus compared to the control group with a statistical power β=0.8 was N=269. For T1-MRI, the equivalent sample size was N=325. Using a dataset of test-retest scans, we show that the measurements were free of additive bias. We also demonstrate that these results were not a confound of certain methodological choices made in the DBM pipeline to address the challenges of making longitudinal measurements from HF-MRI, using a region of interest (ROI) around the HF to globally align serial images, followed by slice-by-slice deformable registration to measure local volume change. Additionally, we present a preliminary study of atrophy rate measurements within hippocampal subfields using HF-MRI. Cross-sectional differences in atrophy rates were detected in several subfields.

Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on pre-selected weighting models and ad hoc methods to choose model parameters. We propose a novel label fusion method to address these limitations. Our formulation directly aims at reducing the expectation of the combined error and can be efficiently solved in a closed form. In our hippocampus segmentation experiment, our method significantly outperforms similarity-based local weighting. Using 20 atlases, we produce results with 0.898 +/- 0.019 Dice overlap to manual labelings for controls.

We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids the numerical instability problem. Experiments on a hippocampus segmentation problem using the ADNI data show that our algorithm consistently converges faster and generalizes better than AdaBoost.

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer ( Fischl et al., 2002 ); hippocampus segmentation using multi-atlas label fusion ( Artaechevarria et al., 2009 ); brain extraction using BET ( Smith, 2002 ); and brain tissue segmentation using FAST ( Zhang et al., 2001 ). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively.

We present and evaluate a new method for automatically labeling the subfields of the hippocampal formation in focal 0.4 × 0.5 × 2.0mm(3) resolution T2-weighted magnetic resonance images that can be acquired in the routine clinical setting with under 5 min scan time. The method combines multi-atlas segmentation, similarity-weighted voting, and a novel learning-based bias correction technique to achieve excellent agreement with manual segmentation. Initial partitioning of MRI slices into hippocampal 'head', 'body' and 'tail' slices is the only input required from the user, necessitated by the nature of the underlying segmentation protocol. Dice overlap between manual and automatic segmentation is above 0.87 for the larger subfields, CA1 and dentate gyrus, and is competitive with the best results for whole-hippocampus segmentation in the literature. Intraclass correlation of volume measurements in CA1 and dentate gyrus is above 0.89. Overlap in smaller hippocampal subfields is lower in magnitude (0.54 for CA2, 0.62 for CA3, 0.77 for subiculum and 0.79 for entorhinal cortex) but comparable to overlap between manual segmentations by trained human raters. These results support the feasibility of subfield-specific hippocampal morphometry in clinical studies of memory and neurodegenerative disease.

We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.

We develop a semi-automatic technique for segmentation of hippocampal subfields in T2-weighted in vivo brain MRI. The technique takes the binary segmentation of the whole hippocampus as input, and automatically labels the subfields inside the hippocampus segmentation. Shape priors for the hippocampal subfields are generated from shape-based normalization of whole hippocampi via the continuous medial representation method. To combine the shape priors with appearance features, we use a machine learning based method. The key novelty is that we treat the mistakes made by the shape priors as bias, which can be detected and corrected via learning. The main advantage of this formulation is that it significantly simplifies the learning problem by taking full advantage of current segmentations and focusing on only improving their drawbacks. Experiments show that the bias removal approach achieves significant improvement in all subfields. Our bias removal idea is general, and can be applied to improve other segmentation methods as well.

This paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization. Evaluation takes place in post-mortem MRI dataset acquired at 9.4 Tesla with many averages and approximately 0.01 mm3 voxel resolution. Continuous medial representations (cm-reps) are used to establish geometrical correspondences between hippocampal formations in different images; the extent to which these correspondences match up subfields is evaluated and compared to normalization driven by image forces. Shape-based normalization is shown to perform only slightly worse than image-based normalization; this is encouraging because the former is more applicable to in vivo MRI, which typically lacks features that distinguish hippocampal subfields.

This paper presents preliminary work on the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu Ammonis fields CA1, CA2/3 and CA4; dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a combination of distinguishable image features and geometrical features. A synthetic average image is derived from the MRI of the samples using shape and intensity averaging in the diffeomorphic non-linear registration framework, and a consensus labeling of the template is generated. The agreement of the consensus labeling with manual labeling of each sample is measured, and the effect of aiding registration with landmarks and manually generated mask images is evaluated.

This paper describes the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu ammonis fields CA1, CA2/3; the dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a combination of distinguishable image features and geometrical features. A synthetic average image is derived from the MRI of the samples using shape and intensity averaging in the diffeomorphic non-linear registration framework, and a consensus labeling of the template is generated. The agreement of the consensus labeling with manual labeling of each sample is measured, and the effect of aiding registration with landmarks and manually generated mask images is evaluated. The atlas is provided as an online resource with the aim of supporting subfield segmentation in emerging hippocampus imaging and image analysis techniques. An example application examining subfield-level hippocampal atrophy in temporal lobe epilepsy demonstrates the application of the atlas to in vivo studies.