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Purpose: Precision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied levels of human interaction. In this work we present two pipelines designed to combine clinical, radiomic (quantified imaging), and RTx-omic (quantified radiation therapy (RT) plan) information for prediction of locoregional failure (LRF) in head and neck cancer (H&N).Methods: Pipelines were designed to extract information and model patient outcomes based on clinical features, computed tomography (CT) imaging, and planned RT dose volumes. We predict H&N LRF using: 1) a highly user-driven pipeline that leverages modular design and machine learning for feature extraction and model development; and 2) a pipeline with minimal user input that utilizes deep learning convolutional neural networks to extract and combine CT imaging, RT dose and clinical features for model development.Results: Clinical features with logistic regression in our highly user-driven pipeline had the highest precision recall area under the curve (PR-AUC) of 0.66 (0.33-0.93), where a PR-AUC = 0.11 is considered random.CONCLUSIONS: Our work demonstrates the potential to aggregate features from multiple specialties for conditional-outcome predictions using pipelines with varied levels of human interaction. Most importantly, our results provide insights into the importance of data curation and quality, as well as user, data and methodology bias awareness as it pertains to result interpretation in user controlled pipelines.

Purpose: Refinement of radiomic results and methodologies is required to ensure progression of the field. In this work, we establish a set of safeguards designed to improve and support current radiomic methodologies through detailed analysis of a radiomic signature.Methods: A radiomic model (MW2018) was fitted and externally validated using features extracted from previously reported lung and head and neck (H&N) cancer datasets using gross-tumour-volume contours, as well as from images with randomly permuted voxel index values; i.e. images without meaningful texture. To determine MW2018's added benefit, the prognostic accuracy of tumour volume alone was calculated as a baseline.Results: MW2018 had an external validation concordance index (c-index) of 0.64. However, a similar performance was achieved using features extracted from images with randomized signal intensities (c-index = 0.64 and 0.60 for H&N and lung, respectively). Tumour volume had a c-index = 0.64 and correlated strongly with three of the four model features. It was determined that the signature was a surrogate for tumour volume and that intensity and texture values were not pertinent for prognostication.Conclusion: Our experiments reveal vulnerabilities in radiomic signature development processes and suggest safeguards that can be used to refine methodologies, and ensure productive radiomic development using objective and independent features. (C) 2018 The Author(s). Published by Elsevier B.V.

Future of Medical Imaging Mark Nadeski and Gene Frantz Ultrahigh-Speed Real-Time Multidimensional Optical Coherence Tomography Kang Zhang and Jin U. Kang Molecular Imaging True Color Spectroscopic (METRiCS) Optical Coherence Tomography Adam Wax and Francisco E. Robles Spatial and Spectral Resolution of Semiconductor Detectors in Medical Imaging Bjorn Heismann Design and Assessment Principles of Semiconductor Flat-Panel Detector-Based X-Ray Micro-CT Systems for Small-Animal Imaging A. Sisniega, J.J. Vaquero, and M. Desco Dual-Energy CT Imaging with Fast-kVp Switching Baojun Li Four-Dimensional Computed Tomography Tinsu Pan Image Reconstruction Algorithms for X-Ray CT Ken Taguchi Portable High-Frequency Ultrasound Imaging System Design and Hardware Considerations Insoo Kim, Hyunsoo Kim, Flavio Griggio, Richard L. Tutwiler, Thomas N. Jackson, Susan Trolier-McKinstry, and Kyusun Choi Recent Advances in Capacitive Micromachined Ultrasonic Transducer Imaging Systems Albert I.H. Chen, Lawrence L.P. Wong, and John T.W. Yeow PET Detectors Alberto Del Guerra and Nicola Belcari Recent Developments of High-Performance PET Detectors Hao Peng and Craig S. Levin CT-SPECT/CT-PET R. Glenn Wells Multimodality Imaging with MR/PET and MR/SPECT Troy H. Farncombe Reducing Respiratory Artifacts in Thoracic PET/CT Greta S.P. Mok, Tao Sun, and Chi Liu Image Reconstruction for 3D PET Jinyi Qi Tracer Kinetic Analysis for PET and SPECT Jae Sung Lee and Dong Soo Lee Multicoil Parallel MRI Angshul Majumdar and Rabab Ward Brain Connectivity Mapping and Analysis Using Diffusion MRI Brian G. Booth and Ghassan Hamarneh T1rho MR Imaging: Principle, Technology, and Application Jing Yuan and Yi-Xiang J. Wang Brain Connectivity Assessed with Functional MRI Aiping Liu, Junning Li, Martin J. McKeown, and Z. Jane Wang Medical Image Registration: A Review Lisa Tang and Ghassan Hamarneh Medical Image Segmentation: Energy Minimization and Deformable Models Chris McIntosh and Ghassan Hamarneh Index

PURPOSE To test the use of well-studied and widely used classification methods alongside newly developed data-filtering techniques specifically designed for imbalanced-data classification in order to demonstrate proof of principle for an automated radiation therapy (RT) quality assurance process on prostate cancer treatment. METHODS A series of acceptable (majority class, n = 61) and erroneous (minority class, n = 12) RT plans as well as a disjoint set of acceptable plans used to develop features (n = 273) were used to develop a dataset for testing. A series of five widely used imbalanced-data classification algorithms were tested with a modularized guided undersampling procedure that includes ensemble-outlier filtering and normalized-cut sampling. RESULTS Hybrid methods including either ensemble-outlier filtering or both filtering and normalized-cut sampling yielded the strongest performance in identifying unacceptable treatment plans. Specifically, five methods demonstrated superior performance in both area under the receiver operating characteristics curve and false positive rate when the true positive rate is equal to one. Furthermore, ensemble-outlier filtering significantly improved results in all but one hybrid method (p < 0.01). Finally, ensemble-outlier filtering methods identified four minority instances that were considered outliers in over 96% of cross-validation iterations. Such instances may be considered distinct planning errors and merit additional inspection, providing potential areas of improvement for the planning process. CONCLUSIONS Traditional imbalanced-data classification methods combined with ensemble-outlier filtering and normalized-cut sampling provide a powerful framework for identifying erroneous RT treatment plans. The proposed methodology yielded strong classification performance and identified problematic instances with high accuracy.

We describe and compare the performance of two types of compact, passively Q-switched Yb:YAG 1030nm lasers and their use for 257nm fourth harmonic generation (FHG). In the first implementation, an end-pumped Yb:YAG laser produced a 250μJ pulse train with an average power at 1030nm of 3.6W. Using a 10mm LBO crystal (70% doubling efficiency), followed by a 7mm BBO crystal (45% conversion efficiency), 1.1W at 257nm was generated (overall FHG efficiency of 30%). The second implementation was a side-pumped Q-switched Yb:YAG laser pumped by a 200W diode bar. A 10mm KTP crystal followed by a 6mm BBO crystal resulted a 15% FHG conversion efficiency. The UV emission was in a form of 1-5 Hz PRF, 2ms long burst of 0.2mJ pulses with a 30kHz intra-burst PRF. Within a 1.65ms emission window, an 11.5mJ burst at 257 nm was generated that had a maximum intra-burst power of 7W. This paper will address the merits of each approach for realizing a man-portable laser suitable for ultraviolet Raman explosives detection.

Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present an atlas-based approach which learns a dose prediction model for each patient (atlas) in a training database, and then learns to match novel patients to the most relevant atlases. The method creates a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces any requirement for specifying dose-volume objectives for conveying the goals of treatment planning. A probabilistic dose distribution is inferred from the most relevant atlases, and is scalarized using a conditional random field to determine the most likely spatial distribution of dose to yield a specific dose prior (histogram) for relevant regions of interest. Voxel-based dose mimicking then converts the predicted dose distribution to a deliverable treatment plan dose distribution. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients. Our preliminary results are promising; automated planning achieved a higher number of dose evaluation criteria in 7 patients and an equal number in 4 patients compared with clinical. Overall, the relative number of criteria achieved was higher for automated planning versus clinical (17 vs 8) and automated planning demonstrated increased sparing for organs at risk (52 vs 44) and better target coverage/uniformity (41 vs 31)., 21 pages, 6 figures, submitted to Physics in Medicine and Biology

We present a compact, side pumped passively Q-switched Yb:YAG laser that was operated in a burst mode with pump durations of 2-4 ms at low duty cycles. Intra-pump pulse Q-switched pulse repetition frequencies varied from 5-20 kHz depending on the transmission of the Cr:YAG saturable absorber, which was varied from 70% to 94%. Pump duration, pulse repetition frequency and output coupler reflectivity were optimized to yield maximum Yb:YAG laser average power and laser efficiency, while providing sufficient peak intensity, typically 0.3-1 MW, to enable efficient forth harmonic generation (FHG). Pulse energies and durations were in ranges of 0.3-1.8 mJ and 1.5-7ns, respectively, dependent on the unbleached transmission of the Cr:YAG saturable absorber. We achieved an optical efficiency of greater than 15% for the Yb:YAG laser. Extra-cavity 515 nm second harmonic generation (SHG) was achieved using a 5mm long KTP crystal. The 515 nm light was then frequency doubled by focusing it into a 7mm long BBO crystal, resulting in a 15% conversion efficiency from 1030nm to 257.5 nm, with an average UV power greater than 100 mW.

A compact 1030nm fiber laser for ultraviolet generation at 257.5nm is presented. The laser employs a short length of highly-doped, large core (20μm), coiled polarization-maintaining ytterbium-doped double-clad fiber pumped by a wavelength-stabilized 975nm diode. It is passively Q-switched via a Cr 4+ :YAG saturable absorber and generates 2.4W at 1030nm in a 110μJ pulse train. Lithium triborate (LBO) and beta-barium borate (BBO) are used to achieve 325mW average power at the fourth harmonic. The laser's small form factor, narrow linewidth and modest power consumption are suitable for use in a man-portable ultraviolet Raman explosives detection system.

We present a novel evolutionary computing based approach to medical image segmentation. Our method complements the image-pixel integration power of deformable shape models with the high-level control mechanisms of genetic algorithms (GA). Specifically, GA alleviate typical deformable model weaknesses pertaining to model initialization, deformation parameter selection, and energy functional local minima through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and driven in-terms of principal modes of variation of a learned mean shape. We demonstrate our work through its application to corpus callosum segmentation in mid-sagittal brain magnetic resonance images (MRI).

The case of a 38-year-old lesbian patient in psychotherapy is presented and discussed. Issues include feelings of disconnection and stagnation in both patient and therapist, and masculine and feminine (butch and femme) gender expression in lesbians.

Despite attacks from Stuxnet to Sony demonstrating the very real IT security threats organisations face, legislation is still slow to act. The EU's data protection legislation, originally due to come into effect this year, has suffered repeated delays. Sources have claimed it has been pushed back to 2016, a date that has already been called unrealistic by those closest to it. In the face of state sponsored cyber-attacks, advanced fraud from cyber-criminals and the damage from single data breaches affecting millions of customers, clearly an organised response to the cyber-security threat is needed. The question now is: who will provide it?

Physical disability in patients with multiple sclerosis is determined by functional ability and quantified with numerical scores. In vivo studies using magnetic resonance imaging (MRI) have found that these scores correlate with spinal cord atrophy (loss of tissue), where atrophy is commonly measured by spinal cord volume or cross-sectional area. However, this correlation is generally weak to moderate, and improved measures would strengthen the utility of imaging biomarkers. We propose novel spinal cord morphological and MRI-based appearance features. Select features are used to train regression models to predict patients’ physical disability scores. We validate our models using 30 MRI scans of different patients with varying levels of disability. Our results suggest that regression models trained with multiple spinal cord features predict clinical disability better than a model based on the volume of the spinal cord alone.

Spinal cord segmentation is an important step to empirically quantify spinal cord atrophy that can occur in neurological diseases such as multiple sclerosis (MS). In this work, we propose a novel method to find the globally optimal segmentation of the spinal cord using a high dimensional minimal path search. The spinal cord cross-sectional shapes are represented using principal component analysis (in the probability simplex) which captures most of spinal cord's axial cross-sectional variation and partial volume effects. We propose modifications to the A* minimal path search algorithm that drastically reduce the required memory and run-time to make our high dimensional minimal path optimization computationally feasible. Finally, we validate our results over five vertebrae levels of both healthy and MS clinical MR volumes (20 volumes total) and show improvements on volume agreement with expert segmentations and less user interaction when compared to current state-of-the-art methods.

We have fabricated prototype frequency tripled Nd:YAG lasers using 808nm Vertical Cavity Surface emitting laser (VCSEL) arrays for end-pumping. The passively Q-switched Nd:YAG laser generated 15mJ pulses with a duration of 2-4 ns. Used as a source for third harmonic generation, the laser produced in excess of 2mJ at 355nm. Of particular concern was the impact of temperature variation on conversion efficiency, which included effects for both the source laser and non-linear crystals. Various solutions to the temperature effects were explored to enable operation of the frequency tripled laser over a wide temperature range.

Radiation therapy is used to treat cancer patients around the world. High quality treatment plans maximally radiate the targets while minimally radiating healthy organs at risk. In order to judge plan quality and safety, segmentations of the targets and organs at risk are created, and the amount of radiation that will be delivered to each structure is estimated prior to treatment. If the targets or organs at risk are mislabelled, or the segmentations are of poor quality, the safety of the radiation doses will be erroneously reviewed and an unsafe plan could proceed. We propose a technique to automatically label groups of segmentations of different structures from a radiation therapy plan for the joint purposes of providing quality assurance and data mining. Given one or more segmentations and an associated image we seek to assign medically meaningful labels to each segmentation and report the confidence of that label. Our method uses random forests to learn joint distributions over the training features, and then exploits a set of learned potential group configurations to build a conditional random field (CRF) that ensures the assignment of labels is consistent across the group of segmentations. The CRF is then solved via a constrained assignment problem. We validate our method on 1574 plans, consisting of 17[Formula: see text] 579 segmentations, demonstrating an overall classification accuracy of 91.58%. Our results also demonstrate the stability of RF with respect to tree depth and the number of splitting variables in large data sets.

We describe generation of 1.1 W of 257 nm emission by frequency quadrupling the 1030 nm emission from a compact passively Q-switched Yb:YAG laser. The laser utilized a volume Bragg grating to achieve a 0.1 nm linewidth required for UV-Raman spectroscopic applications, generated 100 kW peak power, 250 μJ pulses and 3.6 W of average power at 1030 nm. Fourth harmonic generation (FHG) was carried out using a 10 mm lithium triborate (LBO) crystal to generate 515 nm second harmonic with 70% conversion efficiency, followed by a 7 mm beta-barium borate (BBO) crystal to generate 257 nm fourth harmonic with 45% efficiency, resulting in an overall nonlinear conversion efficiency of 31%. Far-field and near-field of the FHG emission were characterized.

We have explored using 808nm Vertical Cavity Surface emitting laser (VCSEL) arrays for end-pumping of Nd:YAG lasers. A variety of laser designs were explored including a compact passively Q-switched lasers that produced a 22mJ pulse having a pulse width of

Image segmentation is often performed via the minimization of an energy function over a domain of possible segmentations. The effectiveness and applicability of such methods depends greatly on the properties of the energy function and its domain, and on what information can be encoded by it. Here we propose an energy function that achieves several important goals. Specifically, our energy function is convex and incorporates shape prior information while simultaneously generating a probabilistic segmentation for multiple regions. Our energy function represents multi-region probabilistic segmentations as elements of a vector space using the isometric log-ratio (ILR) transformation. To our knowledge, these four goals (convex, with shape priors, multi-region, and probabilistic) do not exist together in any other method, and this is the first time ILR is used in an image segmentation method. We provide examples demonstrating the usefulness of these features.

We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose GA to reduce typical deformable model weaknesses pertaining to model initialization, pose estimation and local minima, through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and are driven in terms of localized principal modes of variation, instead of modes of variation across the entire shape that often fail to capture localized shape changes. Although GA are not guaranteed to achieve the global optima, our method compares favorably to the prevalent optimization techniques, convex/nonconvex gradient-based optimizers and to globally optimal graph-theoretic combinatorial optimization techniques, when applied to the task of corpus callosum segmentation in 50 mid-sagittal brain magnetic resonance images.

A method for visualizing manifold-valued medical image data is proposed. The method operates on images in which each pixel is assumed to be sampled from an underlying manifold. For example, each pixel may contain a high dimensional vector, such as the time activity curve (TAC) in a dynamic positron emission tomography (dPET) or a dynamic single photon emission computed tomography (dSPECT) image, or the positive semi-definite tensor in a diffusion tensor magnetic resonance image (DTMRI). A nonlinear mapping reduces the dimensionality of the pixel data to achieve two goals: distance preservation and embedding into a perceptual color space. We use multidimensional scaling distance-preserving mapping to render similar pixels (e.g., DT or TAC pixels) with perceptually similar colors. The 3D CIELAB perceptual color space is adopted as the range of the distance preserving mapping, with a final similarity transform mapping colors to a maximum gamut size. Similarity between pixels is either determined analytically as geodesics on the manifold of pixels or is approximated using manifold learning techniques. In particular, dissimilarity between DTMRI pixels is evaluated via a Log-Euclidean Riemannian metric respecting the manifold of the rank 3, second-order positive semi-definite DTs, whereas the dissimilarity between TACs is approximated via ISOMAP. We demonstrate our approach via artificial high-dimensional, manifold-valued data, as well as case studies of normal and pathological clinical brain and heart DTMRI, dPET, and dSPECT images. Our results demonstrate the effectiveness of our approach in capturing, in a perceptually meaningful way, important features in the data.

A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent computing can bring enormous benefit to existing technology in medical image processing as well as improve medical imaging research. The contributors also cover state-of-the-art research toward integrating medical image processing with artificial intelligence and machine learning approaches.The book presents numerous techniques, algorithms, and models. It describes neural networks, evolutionary optimization techniques, rough sets, support vector machines, tabu search, fuzzy logic, a Bayesian probabilistic framework, a statistical parts-based appearance model, a reinforcement learning-based multistage image segmentation algorithm, a machine learning approach, Monte Carlo simulations, and intelligent, deformable models. The contributors discuss how these techniques are used to classify wound images, extract the boundaries of skin lesions, analyze prostate cancer, handle the inherent uncertainties in mammographic images, and encapsulate the natural intersubject anatomical variance in medical images. They also examine prostate segmentation in transrectal ultrasound images, automatic segmentation and diagnosis of bone scintigraphy, 3-D medical image segmentation, and the reconstruction of SPECT and PET tomographic images.

This paper presents the electrical modeling and characterization of a new chip-to-chip interconnect scheme consisting of miniature coaxial cables. A Micro-Electrical-Mechanical System (MEMS) etching process is used to create trenches along the perimeter of a Silicon substrate to form interface features for the miniature coaxial cables. The coaxial cables use this transition trench to make contact to coplanar transmission lines implemented on-chip. This technique yields a fully impedance matched system for use in high speed signal propagation between dies. This approach promises to deliver higher signaling performance over the traditional wire bond or flip-chip bumping techniques by reducing or eliminating sources of electrical noise such as reflections, cross-talk, and simultaneous switching noise. This paper presents the electrical characterization of prototypes which were fabricated using this interconnect system. Adjacently placed dies are measured when using the new coaxial interconnect approach and compared to measurements of the same system when using wire-bonding. Finite Element Analysis is used to de-embed the system response to yield a comparison of the two interconnect structures (coaxial vs. wire bond).

A method for optical triggering of a Q-switched Nd:YAG laser by direct bleaching of a Cr:YAG saturable absorber is described. This method involves the bleaching of a thin sheet of the saturable absorber from a direction orthogonal to the lasing axis using a single laser diode bar, where the Cr:YAG transmission increased from a non-bleached value of 47% to a bleached value of 63%. For steady state operation of a passively Q-switched laser (PRF=10 Hz), the pulse-to-pulse timing jitter showed approximately 12X reduction in standard deviation, from 241 nsec for free running operation to 20 nsec with optical triggering.

Energy functional minimization is an increasingly popular technique for image segmentation. However, it is far too commonly applied with hand-tuned parameters and initializations that have only been validated for a few images. Fixing these parameters over a set of images assumes the same parameters are ideal for each image. We highlight the effects of varying the parameters and initialization on segmentation accuracy and propose a framework for attaining improved results using image adaptive parameters and initializations. We provide an analytical definition of optimal weights for functional terms through an examination of segmentation in the context of image manifolds, where nearby images on the manifold require similar parameters and similar initializations. Our results validate that fixed parameters are insufficient in addressing the variability in real clinical data, that similar images require similar parameters, and demonstrate how these parameters correlate with the image manifold. We present significantly improved segmentations for synthetic images and a set of 470 clinical examples.

Energy functional minimization is an increasingly popular technique for image segmentation. However, it is far too commonly applied with hand-tuned parameters and initializations that have only been validated for a few images. Fixing these parameters over a set of images assumes the same parameters are ideal for each image. We highlight the effects of varying the parameters and initialization on segmentation accuracy and propose a framework for attaining improved results using image adaptive parameters and initializations. We provide an analytical definition of optimal weights for functional terms through an examination of segmentation in the context of image manifolds, where nearby images on the manifold require similar parameters and similar initializations. Our results validate that fixed parameters are insufficient in addressing the variability in real clinical data, that similar images require similar parameters, and demonstrate how these parameters correlate with the image manifold. We present significantly improved segmentations for synthetic images and a set of 470 clinical examples.

Spinal cord analysis is an important problem relating to the study of various neurological diseases. We present a novel approach to spinal cord segmentation in magnetic resonance images. Our method uses 3D "deformable organisms" (DefOrg) an artificial life framework for medical image analysis that complements classical deformable models (snakes and deformable meshes) with high-level, anatomically-driven control mechanisms. The DefOrg framework allows us to model the organism's body as a growing generalized tubular spring-mass system with an adaptive and predominantly elliptical cross section, and to equip them with spinal cord specific sensory modules, behavioral routines and decision making strategies. The result is a new breed of robust DefOrgs, "spinal crawlers", that crawl along spinal cords in 3D images, accurately segmenting boundaries, and providing sophisticated, clinically-relevant structural analysis. We validate our method through the segmentation of spinal cords in clinical data and provide comparisons to other segmentation techniques.

We outline the development of a self-initializing kinematic tracker that automatically discovers its part appearance models from a video sequence. Through its unique combination of an existing global joint estimation technique and a robust physical deformation based local search method, the tracker is demonstrated as a novel approach to recovering 2D human joint locations and limb outlines from video sequences. Appearance models are discovered and employed through a novel use of the deformable organisms framework which we have extended to the temporal domain. Quantitative and qualitative results for a set of five test videos are provided. The results demonstrate an overall improvement in tracking performance and that the method is relatively insensitive to initialization, an important consideration in gradient descent-style search algorithms.

Medical image analysis is an important problem relating to the study of various diseases. Since their inception to MICCAI in 2001, ‘’deformable organisms’’ have emerged as a fruitful methodology with examples ranging from 2D corpus callosum segmentation to 3D vasculature and spinal cord segmentation. Essentially we have developed an artificial life framework that complements classical deformable models (snakes and deformable meshes) with high-level, anatomically-driven control mechanisms. This paper describes the integration of deformable organisms into the Insight Toolkit (ITK) . Our code attempts to bridge the ITK framework and coding style with deformable organism design methodologies. In the interest of open science, as the framework develops it will serve as a basis for the community to develop new deformable organisms as well as experiment with those recently published by our group.

We present a novel approach to the segmentation and analysis of vasculature from volumetric medical image data. Our method is an adoption and significant extension of deformable organisms, an artificial life framework for medical image analysis that complements classical deformable models with high-level, anatomically-driven control mechanisms. We extend deformable organisms to 3D, model their bodies as tubular spring-mass systems, and equip them with a new repertoire of sensory modules, behavioral routines, and decision making strategies. The result is a new breed of robust deformable organisms, vessel crawlers, that crawl along vasculature in 3D images, accurately segmenting vessel boundaries, detecting and exploring bifurcations, and providing sophisticated, clinically-relevant structural analysis. We validate our method through the segmentation and analysis of vascular structures in both noisy synthetic and real medical image data.

Previously, "Deformable organisms" were introduced as a novel paradigm for medical image analysis that uses artificial life modelling concepts. Deformable organisms were designed to complement the classical bottom-up deformable models methodologies (geometrical and physical layers), with top-down intelligent deformation control mechanisms (behavioral and cognitive layers). However, a true physical layer was absent and in order to complete medical image segmentation tasks, deformable organisms relied on pure geometry-based shape deformations guided by sensory data, prior structural knowledge, and expert-generated schedules of behaviors. In this paper we introduce the use of physics-based shape deformations within the deformable organisms framework yielding additional robustness by allowing intuitive real-time user guidance and interaction when necessary. We present the results of applying our physics-based deformable organisms, with an underlying dynamic spring-mass mesh model, to segmenting and labelling the corpus callosum in 2D midsagittal magnetic resonance images.

A compact, passively Q-switched Nd:YAG laser utilizing a Cr4+:YAG saturable absorber, is end-pumped by the focused emission from an 804 nm vertical-cavity surface-emitting laser (VCSEL) array. By changing the VCSEL operating current, we demonstrated 2x adjustability in the laser output pulse energy, from 9 mJ to 18 mJ. This energy variation was attributed to changes in the angular distribution of VCSEL emission with drive current, resulting in a change in the pump intensity distribution generated by a pump-light-focusing lens.