A Deep Dive into GI Tract Imaging Transformation through Advanced Image Segmentation Analysis with Deep Learning

Abstract

Abstract The reconstruction of computed gastrointestinal tract tomography images has been a vibrant field of study, particularly with the emergence of deep learning techniques. These methods leverage data-driven models to enhance the quality of reconstructions. Our research delves into this domain by conducting a comprehensive data challenge, where various deep learning algorithms were assessed using extensive public datasets. The focal point was on quantitatively evaluating these methods. A noticeable outcome of our investigation is the substantial enhancement in reconstruction quality metrics achieved by deep learning-based approaches, both in applications involving computed tomography (CT) and using methods such as Region-CNN (RCNN) and Conditional Invertible Neural Networks (CINN). We also delve into crucial selection criteria for these methods, encompassing factors like the availability of training data, understanding the physical measurement model, and the speed of reconstruction. The prevailing technique for segmenting three-dimensional tract images relies on convolutional networks and Conditional Invertible Neural Networks. Yet, these advanced architectures, including CNN, RNN, and CINN, impose heavy computational demands, necessitating GPU-accelerated workstations for rapid inference. This research work introduces a novel segmentation method employing a human-like strategy for 3D segmentation where initially analyzes the image at a small scale to pinpoint areas of interest, subsequently processing only pertinent feature-map patches. This innovation drastically reduces inference time and all while upholding state-of-the-art segmentation quality.