Curvature-Sensitive Scheme for Tubular Anatomy Segmentation

Segmenting tubular anatomies from medical images is a difficult task. In addition to all the obstacles typically encountered in the general task of medical image segmentation (obstacles like high inter-subject variability, noise, intra-model differences, etc.), tubular anatomies also have complications which stem from their intrinsic topology. Structures like the airway, aorta, colon, and spine curve in space in unpredictable ways. While this is a challenge in its own right, the problem is perpetuated because this characteristic greatly amplifies the effects of the other difficulties already mentioned (especially anatomical variability).The state-of-the-art in medical image segmentation over the last half decade has been convolutional neural networks. They have dramatically outperformed conventional approaches like multi-atlas segmentation. However, in the context of tubular anatomy segmentation, there is still room for improvement. Our experiments with synthetic data show that the popular convolutional neural network architecture, U-Net struggles to handle the segmentation of long, highly curved tubes, often predicting fragmented segmentations even with considerable training data.In this thesis, we present a new method for handling segmentation of tubular structures that uses both deep learning and conventional techniques in a curvature-sensitive way. Specifically, our method 1) uses a recurrent neural network to unravel tubular anatomies along their centerlines, thereby simplifying the segmentation task and 2) uses classical segmentation technique, region growing in combination with convolutional neural networks to carry out segmentation while maintaining connectivity. We show the effectiveness of our method on synthetic data and on a dataset of high resolution pediatric airway CT scans.Bachelor of Art

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

Data bases and data base systems related to NASA's aerospace program. A bibliography with indexes

This bibliography lists 1778 reports, articles, and other documents introduced into the NASA scientific and technical information system, 1975 through 1980