MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929339189166080 |
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| author | Jha, Debesh Tomar, Nikhil Kumar Biswas, Koushik Durak, Gorkem Antalek, Matthew Zhang, Zheyuan Wang, Bin Rahman, Md Mostafijur Pan, Hongyi Medetalibeyoglu, Alpay Velichko, Yury Ladner, Daniela Borhani, Amir Bagci, Ulas |
| author_facet | Jha, Debesh Tomar, Nikhil Kumar Biswas, Koushik Durak, Gorkem Antalek, Matthew Zhang, Zheyuan Wang, Bin Rahman, Md Mostafijur Pan, Hongyi Medetalibeyoglu, Alpay Velichko, Yury Ladner, Daniela Borhani, Amir Bagci, Ulas |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_06166 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation Jha, Debesh Tomar, Nikhil Kumar Biswas, Koushik Durak, Gorkem Antalek, Matthew Zhang, Zheyuan Wang, Bin Rahman, Md Mostafijur Pan, Hongyi Medetalibeyoglu, Alpay Velichko, Yury Ladner, Daniela Borhani, Amir Bagci, Ulas Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.06166 |