Accurate Colon Segmentation Using 2D Convolutional Neural Networks With 3D Contextual Information

Abstract

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This study introduces an innovative framework designed specifically for accurate colon segmentation in abdomen CT scans, tackling the distinct challenges inherent to this task. Building upon well-established 2D segmentation models, our architecture adeptly incorporates 3D contextual information via a novel method that generates an attention map for a given slice by considering its neighboring slices in a sequence. Our approach accomplishes effective colon segmentation without requiring complex 3D convolutional neural networks (CNNs) or Long Short-Term Memory (LSTM) networks by combining 2D CNNs. Validated on a dataset of 98 CT scans from 49 patients, the architecture exhibits notable performance, successfully capturing nuanced details crucial for precise colon segmentation. The experiments encompass a thorough examination of model selection and cross-validation, providing valuable insights into the efficacy of our proposed approach. The outcomes underscore the potential for streamlined colon segmentation in medical imaging by judiciously integrating 2D and 3D information, employing solely 2D networks, and mitigating challenges associated with 3D networks. The code for model architecture is available at: https://github.com/Samir-Farag/ICIP2024.git