MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation

• URL: http://arxiv.org/abs/2405.06166v1
• Date: Fri, 10 May 2024 01:03:03 GMT
• Title: MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
• Authors: Debesh Jha, Nikhil Kumar Tomar, Koushik Biswas, Gorkem Durak, Matthew Antalek, Zheyuan Zhang, Bin Wang, Md Mostafijur Rahman, Hongyi Pan, Alpay Medetalibeyoglu, Yury Velichko, Daniela Ladner, Amir Borhani, Ulas Bagci,
• Abstract summary: We propose a textbftextitacMDNet to handle challenges of heterogeneity in organ shapes, sizes, and complex anatomical relationships. textitacMDNet is an encoder-decoder network that uses the pre-trained textitMiT-B2 as the encoder and multiple different decoder networks. textitacMDNet is more interpretable and robust compared to the other baseline models.
• Score: 6.4987174473651725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate segmentation of organs from abdominal CT scans is essential for clinical applications such as diagnosis, treatment planning, and patient monitoring. To handle challenges of heterogeneity in organ shapes, sizes, and complex anatomical relationships, we propose a \textbf{\textit{\ac{MDNet}}}, an encoder-decoder network that uses the pre-trained \textit{MiT-B2} as the encoder and multiple different decoder networks. Each decoder network is connected to a different part of the encoder via a multi-scale feature enhancement dilated block. With each decoder, we increase the depth of the network iteratively and refine segmentation masks, enriching feature maps by integrating previous decoders' feature maps. To refine the feature map further, we also utilize the predicted masks from the previous decoder to the current decoder to provide spatial attention across foreground and background regions. MDNet effectively refines the segmentation mask with a high dice similarity coefficient (DSC) of 0.9013 and 0.9169 on the Liver Tumor segmentation (LiTS) and MSD Spleen datasets. Additionally, it reduces Hausdorff distance (HD) to 3.79 for the LiTS dataset and 2.26 for the spleen segmentation dataset, underscoring the precision of MDNet in capturing the complex contours. Moreover, \textit{\ac{MDNet}} is more interpretable and robust compared to the other baseline models.

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