Deconver: A Deconvolutional Network for Medical Image Segmentation
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arXiv
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| Format: | Preprint |
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2025
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| author | Ashtari, Pooya Noei, Shahryar Haredasht, Fateme Nateghi Chen, Jonathan H. Jurman, Giuseppe Pizurica, Aleksandra Van Huffel, Sabine |
| author_facet | Ashtari, Pooya Noei, Shahryar Haredasht, Fateme Nateghi Chen, Jonathan H. Jurman, Giuseppe Pizurica, Aleksandra Van Huffel, Sabine |
| contents | While convolutional neural networks (CNNs) and vision transformers (ViTs) have advanced medical image segmentation, they face inherent limitations such as local receptive fields in CNNs and high computational complexity in ViTs. This paper introduces Deconver, a novel network that integrates traditional deconvolution techniques from image restoration as a core learnable component within a U-shaped architecture. Deconver replaces computationally expensive attention mechanisms with efficient nonnegative deconvolution (NDC) operations, enabling the restoration of high-frequency details while suppressing artifacts. Key innovations include a backpropagation-friendly NDC layer based on a provably monotonic update rule and a parameter-efficient design. Evaluated across four datasets (ISLES'22, BraTS'23, GlaS, FIVES) covering both 2D and 3D segmentation tasks, Deconver achieves state-of-the-art performance in Dice scores and Hausdorff distance while reducing computational costs (FLOPs) by up to 90% compared to leading baselines. By bridging traditional image restoration with deep learning, this work offers a practical solution for high-precision segmentation in resource-constrained clinical workflows. The project is available at https://github.com/pashtari/deconver. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00302 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deconver: A Deconvolutional Network for Medical Image Segmentation Ashtari, Pooya Noei, Shahryar Haredasht, Fateme Nateghi Chen, Jonathan H. Jurman, Giuseppe Pizurica, Aleksandra Van Huffel, Sabine Image and Video Processing Computer Vision and Pattern Recognition While convolutional neural networks (CNNs) and vision transformers (ViTs) have advanced medical image segmentation, they face inherent limitations such as local receptive fields in CNNs and high computational complexity in ViTs. This paper introduces Deconver, a novel network that integrates traditional deconvolution techniques from image restoration as a core learnable component within a U-shaped architecture. Deconver replaces computationally expensive attention mechanisms with efficient nonnegative deconvolution (NDC) operations, enabling the restoration of high-frequency details while suppressing artifacts. Key innovations include a backpropagation-friendly NDC layer based on a provably monotonic update rule and a parameter-efficient design. Evaluated across four datasets (ISLES'22, BraTS'23, GlaS, FIVES) covering both 2D and 3D segmentation tasks, Deconver achieves state-of-the-art performance in Dice scores and Hausdorff distance while reducing computational costs (FLOPs) by up to 90% compared to leading baselines. By bridging traditional image restoration with deep learning, this work offers a practical solution for high-precision segmentation in resource-constrained clinical workflows. The project is available at https://github.com/pashtari/deconver. |
| title | Deconver: A Deconvolutional Network for Medical Image Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.00302 |