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Cold Spring Harbor : Cold Spring Harbor Laboratory, bioRxiv : the preprint server for biology 1-41 (2022). doi:10.1101/2022.03.03.482839, Published by Cold Spring Harbor Laboratory, Cold Spring Harbor

Objective: Diffusion MRI with tractography-based estimation of fiber-connectivity is applied for surgical planning with the aim of maximal safe tumor resection, but preservation of functionally relevant white-matter (WM) tracts. Free-water (FW) partial volume effects however limit tracking algorithms[for full text, please go to the a.m. URL], 71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), 9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie

Hippocampal atrophy is a clinical biomarker of Alzheimer's disease (AD) and is implicated in many other neurological and psychiatric diseases. For this reason, there is much interest in the accurate, reproducible delineation of this region of interest (ROI) in structural MR images. Here, both current and novel MR hippocampal segmentation methods are presented and evaluated: Two versions of FMRIB's Integrated Registration and Segmentation Tool (FIRST and FIRSTv2), Freesurfer's Aseg (FS), Classifier Fusion (CF) and a Fast Marching approach (FMClose). Segmentation performance on two clinical datasets is assessed according to three common measures: Dice coefficient, false positive rate (FPR) and false negative rate (FNR). The first clinical dataset contains 9 normal controls (NC) and 8 highly-atrophied AD patients, whilst the second is a collection of 16 NC and 16 bipolar (BP) patients. Results show that CF outperforms all other methods on the BPSA data, whilst FIRST and FIRSTv2 perform best on the CMA data, with average Dice coefficients of 0.81±0.01, 0.85±0.00 and 0.85±0.01, respectively. This work brings to light several strengths and weaknesses of the evaluated hippocampal segmentation methods, of utmost importance for robust and accurate segmentation in the presence of specific and substantial pathology. © The Eurographics Association 2010.

Introduction/ Background The large size and high resolution of histopathological whole slide images renders their manual annotation time-consuming and costly. State-of-the-art computer-based segmentation approaches are generally able to classify tissue reliably, but strong inter-slide variations between training and evaluation data can cause significant decreases in classification accuracy. Aims In this study, we focus on alpha-SMA stainings of the mouse kidney, and in particular on the classification of glomerular vs. non-glomerular regions. Even though all slides had been recorded using a common staining protocol, inter-slide variations could be observed. We investigate the impact of these variations as well as methods of resolution. Methods We propose an interactive, semi-automatic tissue classification approach [1] which adapts a pre-trained classification model to the new image on which classification should be performed. Image patches for which the class (glomerular/non-glomerular) is uncertain are automatically selected and presented to the user to determine the class label. The user interaction step is repeated several times to iteratively adjust the model to the characteristics of the new image. For image representation and classification, well known methods from the literature are utilized. Specifically, we combine Local Binary Patters with the support vector classifier. Results In case of 50 available labelled sample patches of a certain whole slide image, the overall classification rate increased from 92 % to 98 % through including the interactive labelling step. Even with only 20 labelled patches, accuracy already increased to 97 %. Without a pre-trained model, if training is performed on target domain data only, 88 % (20 labelled samples) and 95 % (50 labelled samples) accuracy, respectively, were obtained. If enough target domain data was available (about 20 images), the amount of source domain data was of minor relevance. The difference in outcome between a source domain training data set containing 100 patches from one whole slide image and a set containing 700 patches from seven images was lower than 1 %. Contrarily, without target domain data, the difference in accuracy was 10 % (82 % compared to 92 %) between these two settings. Execution runtime between two interaction steps is significantly below one second (0.23 s), which is an important usability criterion. It proved to be beneficial to select specific target domain data in an active learning sense based on the currently available trained model. While experimental evaluation provided strong empirical evidence for increased classification performance with the proposed method, the additional manual effort can be kept at a low level. The labelling of e.g. 20 images per slide is surely less time consuming than the validation of a complete whole slide image processed with a fully automatic, but less reliable, segmentation approach. Finally, it should be highlighted that the proposed interaction protocol could easily be adapted to other histopathological classification or segmentation tasks, also for implementation in a clinical system., Diagnostic Pathology, Vol 1 No 8 (2016): 13. European Congress on Digital Pathology

Objective: Diffusion tensor imaging (DTI) based fiber tractography has proven its impact in neurosurgical practice with low postoperative morbidity. However, there is a major drawback for the spatial locations of crossing-, kissing-, or fanning fibers and in the vicinity of tumor or edema. The diffusion[for full text, please go to the a.m. URL], 63. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie (JNS)

With the introduction of diffusion tensor imaging a method became available which is capable to detect major white matter tracts in-vivo. For the visualization of the data several techniques have been developed which, however, show various drawbacks for a comprehensive medical and technical analysis. Although fractional anisotropy maps and streamlines, typically denoted as fiber tracking, are widely used they are suboptimal in several situations of pre- and intraoperative application. Going beyond these standard approaches, several new and more advanced visualization techniques, namely directional volume growing, hulls and hardware-accelerated glyphs are introduced for an improved exploration of the pyramidal tract. The approaches have been evaluated with respect to diagnosis and therapy planning in neurosurgery. Overall, it is shown that the presented strategies for the visualization of diffusion tensor imaging data are capable to significantly support neurosurgical planning and intervention.