Wan, Jiachen;
Dong, Yang;
Yao, Yue;
Xiao, Weijin;
Huang, Ruqi;
Xue, Jing-Hao;
Peng, Ran;
... Ma, Hui; + view all
(2023)
Unsupervised learning of pixel clustering in Mueller matrix images for mapping microstructural features in pathological tissues.
Communications Engineering
, 2
(1)
, Article 88. 10.1038/s44172-023-00136-6.
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Abstract
In histopathology, doctors identify diseases by characterizing abnormal cells and their spatial organization within tissues. Polarization microscopy and supervised learning have been proved as an effective tool for extracting polarization parameters to highlight pathological features. Here, we present an alternative approach based on unsupervised learning to group polarization-pixels into clusters, which correspond to distinct pathological structures. For pathological samples from different patients, it is confirmed that such unsupervised learning technique can decompose the histological structures into a stable basis of characteristic microstructural clusters, some of which correspond to distinctive pathological features for clinical diagnosis. Using hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) samples, we demonstrate how the proposed framework can be utilized for segmentation of histological image, visualization of microstructure composition associated with lesion, and identification of polarization-based microstructure markers that correlates with specific pathology variation. This technique is capable of unraveling invisible microstructures in non-polarization images, and turn them into visible polarization features to pathologists and researchers.
| Type: | Article |
|---|---|
| Title: | Unsupervised learning of pixel clustering in Mueller matrix images for mapping microstructural features in pathological tissues |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s44172-023-00136-6 |
| Publisher version: | http://dx.doi.org/10.1038/s44172-023-00136-6 |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Biomedical engineering, Image processing, Polarization microscopy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10183784 |
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