# Inter-slice Context Residual Learning for 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2011.14155 · HTML: https://mlsanity.com/paper/2011.14155/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.03024/) (2021)
- [Learning from partially labeled data for multi-organ and tumor segmentation](https://mlsanity.com/paper/2211.06894/) (2022)
- [Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective](https://mlsanity.com/paper/2506.10142/) (2025)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [I3Net: Inter-Intra-slice Interpolation Network for Medical Slice Synthesis](https://mlsanity.com/paper/2405.02857/) (2024)
- [Deep Learning for Pancreas Segmentation: a Systematic Review](https://mlsanity.com/paper/2407.16313/) (2024)
- [Medical Image Segmentation Using Directional Window Attention](https://mlsanity.com/paper/2406.17471/) (2024)
- [Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework](https://mlsanity.com/paper/2505.07165/) (2025)
- [Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences](https://mlsanity.com/paper/2106.12153/) (2021)
- [A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans](https://mlsanity.com/paper/2505.01644/) (2025)
- [M-Net: MRI Brain Tumor Sequential Segmentation Network via Mesh-Cast](https://mlsanity.com/paper/2507.20582/) (2025)
- [An atrium segmentation network with location guidance and siamese adjustment](https://mlsanity.com/paper/2301.04401/) (2023)
- [Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments](https://mlsanity.com/paper/2406.07061/) (2024)
- [SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation](https://mlsanity.com/paper/2506.10325/) (2025)
- [ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization](https://mlsanity.com/paper/2512.22570/) (2025)
- [Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer](https://mlsanity.com/paper/2607.22703/) (2026)

## Abstract (arXiv)

Automated and accurate 3D medical image segmentation plays an essential role in assisting medical professionals to evaluate disease progresses and make fast therapeutic schedules. Although deep convolutional neural networks (DCNNs) have widely applied to this task, the accuracy of these models still need to be further improved mainly due to their limited ability to 3D context perception. In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images. This model consists of an encoder, a segmentation decoder, and a context residual decoder. We design the context residual module and use it to bridge both decoders at each scale. Each context residual module contains both context residual mapping and context attention mapping, the formal aims to explicitly learn the inter-slice context information and the latter uses such context as a kind of attention to boost the segmentation accuracy. We evaluated this model on the MICCAI 2018 Brain Tumor Segmentation (BraTS) dataset and NIH Pancreas Segmentation (Pancreas-CT) dataset. Our results not only demonstrate the effectiveness of the proposed 3D context residual learning scheme but also indicate that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top-ranking methods in pancreas segmentation. Code is available at https://git.io/ConResNet