# A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation

arXiv: https://arxiv.org/abs/1712.00201 · HTML: https://mlsanity.com/paper/1712.00201/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 20 of 20)

- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Towards Robust General Medical Image Segmentation](https://mlsanity.com/paper/2107.04263/) (2021)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [LiDARFormer: A Unified Transformer-based Multi-task Network for LiDAR Perception](https://mlsanity.com/paper/2303.12194/) (2023)
- [LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network](https://mlsanity.com/paper/2206.11428/) (2022)
- [Efficient Context-Aware Network for Abdominal Multi-organ Segmentation](https://mlsanity.com/paper/2109.10601/) (2021)
- [APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation](https://mlsanity.com/paper/2210.01485/) (2022)
- [Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework](https://mlsanity.com/paper/2505.07165/) (2025)
- [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)
- [Multi-organ Segmentation Network with Adversarial Performance Validator](https://mlsanity.com/paper/2204.07850/) (2022)
- [UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation](https://mlsanity.com/paper/2209.14378/) (2022)
- [A multi view multi stage and multi window framework for pulmonary artery segmentation from CT scans](https://mlsanity.com/paper/2209.03918/) (2022)
- [Detection and Segmentation of Pancreas using Morphological Snakes and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2302.06356/) (2023)
- [Curriculum Knowledge Switching for Pancreas Segmentation](https://mlsanity.com/paper/2306.12651/) (2023)
- [M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans](https://mlsanity.com/paper/2401.10419/) (2024)
- [Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning](https://mlsanity.com/paper/2405.12367/) (2024)
- [SpineContextResUNet: A Computationally Efficient Residual UNet for Spine CT Segmentation](https://mlsanity.com/paper/2605.20760/) (2026)
- [DCSNet: Multiscale Feature Aggregation for Small Medical Object Segmentation with Detection-guided Hierarchical Cropping](https://mlsanity.com/paper/2606.28402/) (2026)

## Abstract (arXiv)

In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D tasks due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarse-to-fine framework to effectively and efficiently tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial infor- mation along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the current state-of-the-art in terms of Dice-Sorensen Coefficient (DSC). On the NIH pancreas segmentation dataset, we outperform the previous best by an average of over 2%, and the worst case is improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.