AI-Based Universal Lesion Segmentation Application for Thoracic-Abdominal in Computed Tomography Scans

Abstract

Medical imaging plays a crucial role in the diagnosis and treatment of various diseases, particularly in identifying and evaluating lesions within the thoracic-abdominal region. Traditional methods of lesion segmentation, which rely heavily on manual delineation by radiologists, are time-consuming and prone to variability. Advances in deep learning and artificial intelligence offer promising solutions to these challenges by automating the segmentation process, thus enhancing accuracy and efficiency. This master thesis presents an innovative framework for universal lesion segmentation in thoracic-abdominal computed tomography (CT) scans using advanced deep learning techniques. The proposed methodology leverages the U-Mamba model, which integrates Convolutional Neural Networks (CNNs) and State Space Models (SSMs), to enhance segmentation accuracy and efficiency. The framework involves meticulous preprocessing steps, including lesion selection and Volume of Interest (VOI) cropping, to ensure precise targeting of lesions. The U-Mamba

model, trained with the LION (EvoLved Sign Momentum) optimizer, demonstrates su- perior performance in capturing long-range dependencies and handling complex lesion

structures across various datasets. The model’s encoder-decoder architecture, coupled

with residual blocks and skip connections, enables robust and detailed segmentation out- puts. This study validates the efficacy of the proposed framework through comprehensive

evaluations, underscoring its potential to improve diagnostic imaging and clinical work- flows.