Solution to the Kidney Tumor Segmentation Challenge 2019

Kidney and kidney tumor segmentation are essential steps in kidney cancer surgery. In this paper, we focus on addressing hard cases and exploring the kidney tumor shape prior rather than developing new convolution neural network architectures. Specifically, we train additional tumor segmentation networks to bias the ensemble classifier to tumor. Moreover, we propose the compact loss function to constrain the shape of the tumor segmentation results. Experiments on KiTS challenge show that both hard mining and compact can improve the performance of U-Net baseline

How molecular imaging will enable robotic precision surgery: the role of artificial intelligence, augmented reality, and navigation

Molecular imaging is one of the pillars of precision surgery. Its applications range from early diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In particular, molecular imaging solutions are in high demand in minimally invasive surgical strategies, such as the substantially increasing field of robotic surgery. This review aims at connecting the molecular imaging and nuclear medicine community to the rapidly expanding armory of surgical medical devices. Such devices entail technologies ranging from artificial intelligence and computer-aided visualization technologies (software) to innovative molecular imaging modalities and surgical navigation (hardware). We discuss technologies based on their role at different steps of the surgical workflow, i.e., from surgical decision and planning, over to target localization and excision guidance, all the way to (back table) surgical verification. This provides a glimpse of how innovations from the technology fields can realize an exciting future for the molecular imaging and surgery communities.Imaging- and therapeutic targets in neoplastic and musculoskeletal inflammatory diseas

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

The Functional, Ecological, and Evolutionary Morphology of Sea Lampreys (Petromyzon marinus)

Lampreys (Petromyzontiformes) are jawless vertebrates with an evolutionary history lasting at least 360 million years and are often used in comparisons with jawed vertebrates because some of their morphological aspects, such as the segmented trunk musculature with curved myosepta and a non-mineralized skeleton fibrous skeleton, are thought to resemble the condition of early vertebrates before the evolution of jaws. Although earlier authors studied the morphology of the skeleto-muscular system of the trunk of lampreys, their studies are not detailed and complete enough to allow a functional and biomechanical analysis that is needed as a basis for modeling the mechanics of lamprey locomotion and for understanding the causal roles played by the anatomical structures within the trunk. Questions remain, such as what is the architecture of the trunk fibroskeleton, and how does it function with the musculature to bend the trunk? This dissertation studied the functional, ecological and evolutionary morphology of the trunk of Sea Lampreys (Petromyzon marinus) as well as its relevance in understanding the environmental history of landlocked lamprey populations. Functional morphology revealed that the fibroskeleton of the trunk is a self-supporting concatenated system of fibers, which creates a scaffold for the musculature and transmits forces to bend the trunk during swimming. Ecological morphology demonstrated the adaptive advantage of the fibroskeleton’s architecture, which enables the movements that are performed during migration and spawning and gives lampreys the capacity to colonize upstream realms. These results help explain the evolutionary morphology of lampreys, which likely originated in freshwater as algal feeders and evolved into parasites after going through an intermediary scavenging stage. When these insights are applied to the evolution of landlocked Sea Lampreys, it becomes evident that their entry into freshwater lakes occurred as soon as they were able to reach them and that populations likely became established in Lake Ontario, Lake Champlain, and the Finger Lakes thousands of years ago. This insight undermines the current status of landlocked Sea Lampreys as invasive species in these lakes and the case for their eradication. Hence, this dissertation provides a comprehensive and integrative analysis of lamprey biology from their anatomy to environmental policy

Deep learning-based affine and deformable 3D medical image registration

In medical image registration, medical scans are transformed to align their image content. Traditionally, image registration is performed manually by clinicians or using optimization-based algorithms, but in the past few years, deep learning has been successfully applied to the problem. In this work, deep learning image registration (DLIR) methods were compared on the task of aligning inter- and intra-patient male pelvic full field-of-view 3D Computed Tomography (CT) scans. The multistage registration pipeline used consisted of a cascade of an affine (global) registration and a deformable (local) registration. For the affine registration step, a 3D ResNet model was used. The two deformable methods that were investigated are VoxelMorph, the most commonly used DLIR framework, and LapIRN, a recent multi-resolution DLIR method. The two registration steps were trained separately; For the affine registration step, both supervised and unsupervised learning methods were employed. For the deformable step, unsupervised learning and weakly supervised learning using masks of regions of interest (ROIs) were used. The training was done on synthetically augmented CT scans. The results were compared to results obtained with two top-performing iterative image registration frameworks. The evaluation was based on ROI similarity of the registered scans, as well as diffeomorphic properties and runtime of the registration. Overall, the DLIR methods were not able to outperform the baseline iterative methods. The affine step followed by deformable registration with LaPIRN managed to perform similarly to or slightly worse than the baseline methods, managing to outperform them on 7 out of 12 ROIs on the intra-patient scans. The inter-patient registration task turned out to be challenging, with none of the methods performing well consistently. For both tasks, the DLIR methods achieve a very significant time speedup compared to the baseline methods