# Deep learning and its application to medical image segmentation

arXiv: https://arxiv.org/abs/1803.08691 · HTML: https://mlsanity.com/paper/1803.08691/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 13 of 13)

- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.04971/) (2024)
- [Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery](https://mlsanity.com/paper/2402.01974/) (2024)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods](https://mlsanity.com/paper/2408.03393/) (2024)
- [Self Pre-training with Adaptive Mask Autoencoders for Variable-Contrast 3D Medical Imaging](https://mlsanity.com/paper/2501.09096/) (2025)
- [The SAM2-to-SAM3 Gap in the Segment Anything Model Family: Why Prompt-Based Expertise Fails in Concept-Driven Image Segmentation](https://mlsanity.com/paper/2512.06032/) (2025)
- [A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification](https://mlsanity.com/paper/2202.06465/) (2022)
- [Multiclass Semantic Segmentation to Identify Anatomical Sub-Regions of Brain and Measure Neuronal Health in Parkinson's Disease](https://mlsanity.com/paper/2301.02925/) (2023)
- [Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors](https://mlsanity.com/paper/2308.05068/) (2023)
- [Multi-Plane Vision Transformer for Hemorrhage Classification Using Axial and Sagittal MRI Data](https://mlsanity.com/paper/2505.07349/) (2025)
- [VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation](https://mlsanity.com/paper/2505.08693/) (2025)
- [TA-LSDiff:Topology-Aware Diffusion Guided by a Level Set Energy for Pancreas Segmentation](https://mlsanity.com/paper/2511.00815/) (2025)

## Abstract (arXiv)

One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been proven very challenging due to the large variation of anatomy across different patients. However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision. Due to the data driven approaches of hierarchical feature learning in deep learning frameworks, these advances can be translated to medical images without much difficulty. Several variations of deep convolutional neural networks have been successfully applied to medical images. Especially fully convolutional architectures have been proven efficient for segmentation of 3D medical images. In this article, we describe how to build a 3D fully convolutional network (FCN) that can process 3D images in order to produce automatic semantic segmentations. The model is trained and evaluated on a clinical computed tomography (CT) dataset and shows state-of-the-art performance in multi-organ segmentation.