TOWARD SOLVING GROUPWISE MEDICAL IMAGE ANALYSIS PROBLEMS WITH DEEP LEARNING

Image regression, atlas building, and multi-atlas segmentation are three groupwise medical image analysis problems extended from image registration. These three problems are challenging because of the difficulty in establishing spatial correspondences and the associated high computational cost. Specifically, most previous methods are computationally costly as they are optimization-based approaches. Hence fast and accurate approaches are highly desirable. This dissertation addresses the following problems concerning the three groupwise medical im- age analysis problems: (1) fast and reliable geodesic regression for image time series; (2) joint atlas building and diffeomorphic registration learning; (3) efficient and accurate label fusion for multi-atlas segmentation; and (4) spatially localized probability calibration for semantic segmentation networks. Specifically, the contributions in this thesis are as follows: (1) A fast predictive simple geodesic regression approach is proposed to capture the frequently subtle deformation trends of longitudinal image data. (2) A new deep learning model that jointly builds an atlas and learns the diffeomorphic registrations in both the atlas-to-image and the image-to-atlas directions is developed. (3) A novel deep learning label fusion method (VoteNet) that locally identifies sets of trustworthy atlases is presented; and several ways to improve the performance under the VoteNet based multi-atlas segmentation framework are explored. (4) A learning-based local temperature scaling method that predicts a separate temperature scale for each pixel/voxel is designed. The resulting post-processing approach is accuracy preserving and is theoretically guaranteed to be effective.Doctor of Philosoph

Laparoscopic Management of Perforated Meckel's Diverticulum in Adults

Objective: To determine the role of laparoscopy in diagnosis and surgical treatment of perforated Meckel's diverticulum (MD) in adults

Mechanism of crack propagation for K9 glass

In order to study the mechanism of crack propagation, the varied cutting-depth scratch experiment is carried out and smoothed particle hydrodynamics (SPH) simulation method is used to assistant the investigation. The SPH simulation results reveal that crack will propagate in the direction where stress concentration exceeds the fracture toughness of K9 glass. The initial crack length in critical transition depth is calculated by combining the critical stress of fracture and the fracture toughness of K9 glass. Based on the effective plastic strain, the relation between scratching depth and crack depth is obtained. The recovery of crack tip is found and explained from the relationship between cutting depth and crack depth. Using the energy balance theory of Griffith, the variation of material internal energy is revealed. Comparing the scratching forces obtained from experiment and simulation, the validity of simulation results is verified. The phenomenon of crack delayed propagation is found in both experiment and simulation. The explanation of mechanism is given