ArticleIEEE transactions on medical imaging2023
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI.
Article in IEEE transactions on medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Anatomy-aware fusion attention network for high-precision prostate MRI segmentation.Medical physics · 2026Article
- Retinal layer segmentation in OCT images with a 2.5D cross-slice feature fusion module for glaucoma assessment.Biomedical optics express · 2026Article
- PCa-Mamba: Spatiotemporal state space models for prostate cancer detection in multi-parametric MRI.Medical image analysis · 2026Article
- ProSeg: multi-scale context fusion for high-precision prostate segmentation in MRI.Scientific reports · 2026Article
- Semi-supervised Medical Image Segmentation Using Heterogeneous Complementary Correction Network and Confidence Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- Article
- Current state of the art of new prostate MRI technologies and potential future developments.BJR open · 2026Review
- Multi-attention Mechanism for Enhanced Pseudo-3D Prostate Zonal Segmentation.Journal of imaging informatics in medicine · 2025Article
- A Neural Conditional Random Field Model Using Deep Features and Learnable Functions for End-to-End MRI Prostate Zonal Segmentation.The journal of machine learning for biomedical imaging · 2025Article
- SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model.Journal of imaging · 2025Article
- MPR-DIFF: A SELF-SUPERVISED DIFFUSION MODEL FOR MULTI-PLANAR REFORMATION IN PROSTATE MICRO-ULTRASOUND IMAGING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- MRI Super-Resolution With Partial Diffusion Models.IEEE transactions on medical imaging · 2025Article
- Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context Modeling.Journal of imaging · 2025Article
- RaNet: a residual attention network for accurate prostate segmentation in T2-weighted MRI.Frontiers in medicine · 2025Article
- Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- A mixed Mamba U-net for prostate segmentation in MR images.Scientific reports · 2024Article
- A Comparative Analysis of U-Net and Vision Transformer Architectures in Semi-Supervised Prostate Zonal Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
- AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI.Scientific reports · 2024Article
- DECTNet: Dual Encoder Network combined convolution and Transformer architecture for medical image segmentation.PloS one · 2024Article
- CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by leveraging important multi-scale image-based information from adjacent slices, but current methods do not fully learn and exploit such cross-slice information. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn cross-slice information at multiple scales. The module can be utilized in any existing deep-learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture cross-slice information significant for prostate zonal segmentation in order to improve the performance of current state-of-the-art methods. Cross-slice attention improves segmentation accuracy in the peripheral zones, such that segmentation results are consistent across all the prostate slices (apex, mid-gland, and base). The code for the proposed model is available at https://bit.ly/CAT-Net.
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