# A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation

arXiv: https://arxiv.org/abs/1806.02237 · HTML: https://mlsanity.com/paper/1806.02237/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 17 of 17)

- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2201.00462/) (2022)
- [Learning from partially labeled data for multi-organ and tumor segmentation](https://mlsanity.com/paper/2211.06894/) (2022)
- [A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2309.03906/) (2023)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation](https://mlsanity.com/paper/2106.01596/) (2021)
- [Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation](https://mlsanity.com/paper/2201.00317/) (2022)
- [Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentation](https://mlsanity.com/paper/2403.07303/) (2024)
- [Characterizing Renal Structures with 3D Block Aggregate Transformers](https://mlsanity.com/paper/2203.02430/) (2022)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images](https://mlsanity.com/paper/2205.15941/) (2022)
- [UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation](https://mlsanity.com/paper/2209.14378/) (2022)
- [Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation](https://mlsanity.com/paper/2210.08652/) (2022)
- [Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound](https://mlsanity.com/paper/2107.13833/) (2021)
- [Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation](https://mlsanity.com/paper/2205.05898/) (2022)
- [SAS: Segment Anything Small for Ultrasound -- A Non-Generative Data Augmentation Technique for Robust Deep Learning in Ultrasound Imaging](https://mlsanity.com/paper/2503.05916/) (2025)
- [Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment](https://mlsanity.com/paper/2602.21703/) (2026)

## Abstract (arXiv)

Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images. However, most network architectures require severely downsampling or cropping the images to meet the memory limitations of today's GPU cards while still considering enough context in the images for accurate segmentation. In this work, we propose a novel approach that utilizes auto-context to perform semantic segmentation at higher resolutions in a multi-scale pyramid of stacked 3D FCNs. We train and validate our models on a dataset of manually annotated abdominal organs and vessels from 377 clinical CT images used in gastric surgery, and achieve promising results with close to 90% Dice score on average. For additional evaluation, we perform separate testing on datasets from different sources and achieve competitive results, illustrating the robustness of the model and approach.