Kidney and Kidney-tumor Segmentation Using Cascaded V-Nets

Kidney cancer is the seventh most common cancer worldwide, accounting for an estimated 140,000 global deaths annually. Kidney segmentation in volumetric medical images plays an important role in clinical diagnosis, radiotherapy planning, interventional guidance and patient follow-ups however, to our knowledge, there is no automatic kidneytumor segmentation method present in the literature. In this paper, we address the challenge of simultaneous semantic segmentation of kidney and tumor by adopting a cascaded V-Net framework. The first V-Net in our pipeline produces a region of interest around the probable location of the kidney and tumor, which facilitates the removal of the unwanted region in the CT volume. The second sets of V-Nets are trained separately for the kidney and tumor, which produces the kidney and tumor masks respectively. The final segmentation is achieved by combining the kidney and tumor mask together. Our method is trained and validated on 190 and 20 patients scans, respectively, accesses from 2019 Kidney Tumor Segmentation Challenge database. We achieved a validation accuracy in terms of the Sørensen Dice coefficient of about 97%

Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation

Image segmentation is a fundamental and challenging problem in computer vision with applications spanning multiple areas, such as medical imaging, remote sensing, and autonomous vehicles. Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines. Although CNN-based models are adept at learning abstract features from raw image data, their performance is dependent on the availability and size of suitable training datasets. Additionally, these models are often unable to capture the details of object boundaries and generalize poorly to unseen classes. In this thesis, we devise novel methodologies that address these issues and establish robust representation learning frameworks for fully-automatic semantic segmentation in medical imaging and mainstream computer vision. In particular, our contributions include (1) state-of-the-art 2D and 3D image segmentation networks for computer vision and medical image analysis, (2) an end-to-end trainable image segmentation framework that unifies CNNs and active contour models with learnable parameters for fast and robust object delineation, (3) a novel approach for disentangling edge and texture processing in segmentation networks, and (4) a novel few-shot learning model in both supervised settings and semi-supervised settings where synergies between latent and image spaces are leveraged to learn to segment images given limited training data.Comment: PhD dissertation, UCLA, 202

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd