Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study
| Autor: | Mark Panzenbeck, Jie Zheng, Di Feng, Dechao Shan, Alexander C. Klimowicz, Zheng Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Entropy
computer.software_genre Epithelium Machine Learning Animal Cells Medicine and Health Sciences Image Processing Computer-Assisted Medical Personnel Tissue homeostasis Multidisciplinary Physics Representation (systemics) Colitis Professions Proof of concept Physical Sciences Medicine Unsupervised learning Thermodynamics Anatomy Cellular Types Research Article Computer and Information Sciences Histology Colon Science Gastroenterology and Hepatology Machine learning Deep Learning In vivo Artificial Intelligence Pathological business.industry Deep learning Inflammatory Bowel Disease Biology and Life Sciences Epithelial Cells Image segmentation Cell Biology Gastrointestinal Tract Pathologists Biological Tissue People and Places Population Groupings Artificial intelligence business computer Digestive System |
| Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 6, p e0252429 (2021) |
| ISSN: | 1932-6203 |
| Popis: | Applying deep learning to the field of preclinical in vivo studies is a new and exciting prospect with the potential to unlock decades worth of underutilized data. As a proof of concept, we performed a feasibility study on a colitis model treated with Sulfasalazine, a drug used in therapeutic care of inflammatory bowel disease. We aimed to evaluate the colonic mucosa improvement associated with the recovery response of the crypts, a complex histologic structure reflecting tissue homeostasis and repair in response to inflammation. Our approach requires robust image segmentation of objects of interest from whole slide images, a composite low dimensional representation of the typical or novel morphological variants of the segmented objects, and exploration of image features of significance towards biology and treatment efficacy. Both interpretable features (eg. counts, area, distance and angle) as well as statistical texture features calculated using Gray Level Co-Occurance Matrices (GLCMs), are shown to have significance in analysis. Ultimately, this analytic framework of supervised image segmentation, unsupervised learning, and feature analysis can be generally applied to preclinical data. We hope our report will inspire more efforts to utilize deep learning in preclinical in vivo studies and ultimately make the field more innovative and efficient. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|