# Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning

arXiv: https://arxiv.org/abs/1804.03830 · HTML: https://mlsanity.com/paper/1804.03830/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation](https://mlsanity.com/paper/2107.04934/) (2021)
- [Contrastive Representation Learning for Gaze Estimation](https://mlsanity.com/paper/2210.13404/) (2022)
- [DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization](https://mlsanity.com/paper/2110.00109/) (2021)
- [Self-supervised Semantic Segmentation: Consistency over Transformation](https://mlsanity.com/paper/2309.00143/) (2023)
- [Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features](https://mlsanity.com/paper/2305.00067/) (2023)
- [Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation](https://mlsanity.com/paper/2105.07715/) (2021)
- [Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering](https://mlsanity.com/paper/2307.02462/) (2023)
- [Physics Driven Domain Specific Transporter Framework with Attention Mechanism for Ultrasound Imaging](https://mlsanity.com/paper/2109.06346/) (2021)
- [Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease](https://mlsanity.com/paper/2507.12012/) (2025)

## Abstract (arXiv)

This paper presents a novel unsupervised segmentation method for 3D medical images. Convolutional neural networks (CNNs) have brought significant advances in image segmentation. However, most of the recent methods rely on supervised learning, which requires large amounts of manually annotated data. Thus, it is challenging for these methods to cope with the growing amount of medical images. This paper proposes a unified approach to unsupervised deep representation learning and clustering for segmentation. Our proposed method consists of two phases. In the first phase, we learn deep feature representations of training patches from a target image using joint unsupervised learning (JULE) that alternately clusters representations generated by a CNN and updates the CNN parameters using cluster labels as supervisory signals. We extend JULE to 3D medical images by utilizing 3D convolutions throughout the CNN architecture. In the second phase, we apply k-means to the deep representations from the trained CNN and then project cluster labels to the target image in order to obtain the fully segmented image. We evaluated our methods on three images of lung cancer specimens scanned with micro-computed tomography (micro-CT). The automatic segmentation of pathological regions in micro-CT could further contribute to the pathological examination process. Hence, we aim to automatically divide each image into the regions of invasive carcinoma, noninvasive carcinoma, and normal tissue. Our experiments show the potential abilities of unsupervised deep representation learning for medical image segmentation.