ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation

• URL: http://arxiv.org/abs/2506.19687v1
• Date: Tue, 24 Jun 2025 14:56:55 GMT
• Title: ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation
• Authors: Ahmad Mustafa, Reza Rastegar, Ghassan AlRegib,
• Abstract summary: We propose a hybrid architecture that models MRI sequences as annotated data.<n>Our method uses a deep, preserving pretrained DeepVLab3 backbone to extract high-level semantic features from each MRI slice and a recurrent convolutional head, built with ConvLSTM layers, to integrate information across slices.<n>Compared to state-of-the-art 2D and 3D segmentation models, our approach demonstrates superior performance in terms of precision, recall, Intersection over Union (IoU), Dice Similarity Coefficient (DSC) and robustness.
• Score: 11.248082139905865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prostate gland segmentation from T2-weighted MRI is a critical yet challenging task in clinical prostate cancer assessment. While deep learning-based methods have significantly advanced automated segmentation, most conventional approaches-particularly 2D convolutional neural networks (CNNs)-fail to leverage inter-slice anatomical continuity, limiting their accuracy and robustness. Fully 3D models offer improved spatial coherence but require large amounts of annotated data, which is often impractical in clinical settings. To address these limitations, we propose a hybrid architecture that models MRI sequences as spatiotemporal data. Our method uses a deep, pretrained DeepLabV3 backbone to extract high-level semantic features from each MRI slice and a recurrent convolutional head, built with ConvLSTM layers, to integrate information across slices while preserving spatial structure. This combination enables context-aware segmentation with improved consistency, particularly in data-limited and noisy imaging conditions. We evaluate our method on the PROMISE12 benchmark under both clean and contrast-degraded test settings. Compared to state-of-the-art 2D and 3D segmentation models, our approach demonstrates superior performance in terms of precision, recall, Intersection over Union (IoU), and Dice Similarity Coefficient (DSC), highlighting its potential for robust clinical deployment.

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