ArticleIEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision
Multi-Aperture Transformers for 3D (MAT3D) Segmentation of Clinical and Microscopic Images.
Article in IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Robust analysis of the tumor spectrum in a preclinical model of breast cancer reveals stable subtypes with distinct growth patterns.Computer methods and programs in biomedicine · 2026Article
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5 authors.
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Abstract
3D segmentation of biological structures is critical in biomedical imaging, offering significant insights into structures and functions. This paper introduces a novel segmentation of biological images that couples Multi-Aperture representation with Transformers for 3D (MAT3D) segmentation. Our method integrates the global context-awareness of Transformer networks with the local feature extraction capabilities of Convolutional Neural Networks (CNNs), providing a comprehensive solution for accurately delineating complex biological structures. First, we evaluated the performance of the proposed technique on two public clinical datasets of ACDC and Synapse multi-organ segmentation, rendering superior Dice scores of 93.34±0.05 and 89.73±0.04, respectively, with fewer parameters compared to the published literature. Next, we assessed the performance of our technique on an organoid dataset comprising four breast cancer subtypes. The proposed method achieved a Dice 95.12±0.02 and a PQ score of 97.01±0.01, respectively. MAT3D also significantly reduces the parameters to 40 million. The code is available on https://github.com/sohaibcs1/MAT3D.
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Registered trials
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