Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features
<italic>Goal</italic>: The early diagnosis and treatment of hepatitis is essential to reduce hepatitis-related liver function deterioration and mortality. One component of the widely-used Ishak grading system for the grading of periportal interface hepatitis is based on the percentage of...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10476647/ |
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| author | Hung-Wen Tsai Chien-Yu Chiou Wei-Jong Yang Tsan-An Hsieh Cheng-Yi Chen Che-Wei Hsu Yih-Jyh Lin Min-En Hsieh Matthew M. Yeh Chin-Chun Chen Meng-Ru Shen Pau-Choo Chung |
| author_facet | Hung-Wen Tsai Chien-Yu Chiou Wei-Jong Yang Tsan-An Hsieh Cheng-Yi Chen Che-Wei Hsu Yih-Jyh Lin Min-En Hsieh Matthew M. Yeh Chin-Chun Chen Meng-Ru Shen Pau-Choo Chung |
| author_sort | Hung-Wen Tsai |
| collection | DOAJ |
| description | <italic>Goal</italic>: The early diagnosis and treatment of hepatitis is essential to reduce hepatitis-related liver function deterioration and mortality. One component of the widely-used Ishak grading system for the grading of periportal interface hepatitis is based on the percentage of portal borders infiltrated by lymphocytes. Thus, the accurate detection of lymphocyte-infiltrated periportal regions is critical in the diagnosis of hepatitis. However, the infiltrating lymphocytes usually result in the formation of ambiguous and highly-irregular portal boundaries, and thus identifying the infiltrated portal boundary regions precisely using automated methods is challenging. This study aims to develop a deep-learning-based automatic detection framework to assist diagnosis. <italic>Methods</italic>: The present study proposes a framework consisting of a Structurally-REfined Deep Portal Segmentation module and an Infiltrated Periportal Region Detection module based on heterogeneous infiltration features to accurately identify the infiltrated periportal regions in liver Whole Slide Images. <italic>Results</italic>: The proposed method achieves 0.725 in F1-score of lymphocyte-infiltrated periportal region detection. Moreover, the statistics of the ratio of the detected infiltrated portal boundary have high correlation to the Ishak grade (Spearman's correlations more than 0.87 with p-values less than 0.001) and medium correlation to the liver function index aspartate aminotransferase and alanine aminotransferase (Spearman's correlations more than 0.63 and 0.57 with p-values less than 0.001). <italic>Conclusions</italic>: The study shows the statistics of the ratio of infiltrated portal boundary have correlation to the Ishak grade and liver function index. The proposed framework provides pathologists with a useful and reliable tool for hepatitis diagnosis. |
| format | Article |
| id | doaj-art-39b885ccbc2b47bab366ff030aa2c10c |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-39b885ccbc2b47bab366ff030aa2c10c2025-08-20T03:33:11ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01526127010.1109/OJEMB.2024.337947910476647Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration FeaturesHung-Wen Tsai0https://orcid.org/0000-0001-9223-2535Chien-Yu Chiou1https://orcid.org/0000-0002-6737-2963Wei-Jong Yang2https://orcid.org/0000-0002-4738-4617Tsan-An Hsieh3https://orcid.org/0009-0007-3694-0917Cheng-Yi Chen4Che-Wei Hsu5https://orcid.org/0000-0002-6742-9835Yih-Jyh Lin6https://orcid.org/0000-0003-0998-3229Min-En Hsieh7https://orcid.org/0009-0001-9703-8456Matthew M. Yeh8Chin-Chun Chen9https://orcid.org/0009-0006-4604-2961Meng-Ru Shen10https://orcid.org/0000-0003-0506-3505Pau-Choo Chung11https://orcid.org/0000-0002-8660-570XDepartment of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, TaiwanDepartment of Electrical Engineering, National Cheng Kung University, Tainan, TaiwanDepartment of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung, TaiwanInstitute of Computer and Communication Engineering, National Cheng Kung University, Tainan, TaiwanDepartment of Cell Biology and Anatomy, College of Medicine, National Cheng Kung University, Tainan, TaiwanDepartment of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, TaiwanDepartment of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, TaiwanDepartment of Electrical Engineering, National Cheng Kung University, Tainan, TaiwanDepartment of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USADepartment of Statistics, National Cheng Kung University, Tainan, TaiwanDepartment of Pharmacology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, TaiwanDepartment of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan<italic>Goal</italic>: The early diagnosis and treatment of hepatitis is essential to reduce hepatitis-related liver function deterioration and mortality. One component of the widely-used Ishak grading system for the grading of periportal interface hepatitis is based on the percentage of portal borders infiltrated by lymphocytes. Thus, the accurate detection of lymphocyte-infiltrated periportal regions is critical in the diagnosis of hepatitis. However, the infiltrating lymphocytes usually result in the formation of ambiguous and highly-irregular portal boundaries, and thus identifying the infiltrated portal boundary regions precisely using automated methods is challenging. This study aims to develop a deep-learning-based automatic detection framework to assist diagnosis. <italic>Methods</italic>: The present study proposes a framework consisting of a Structurally-REfined Deep Portal Segmentation module and an Infiltrated Periportal Region Detection module based on heterogeneous infiltration features to accurately identify the infiltrated periportal regions in liver Whole Slide Images. <italic>Results</italic>: The proposed method achieves 0.725 in F1-score of lymphocyte-infiltrated periportal region detection. Moreover, the statistics of the ratio of the detected infiltrated portal boundary have high correlation to the Ishak grade (Spearman's correlations more than 0.87 with p-values less than 0.001) and medium correlation to the liver function index aspartate aminotransferase and alanine aminotransferase (Spearman's correlations more than 0.63 and 0.57 with p-values less than 0.001). <italic>Conclusions</italic>: The study shows the statistics of the ratio of infiltrated portal boundary have correlation to the Ishak grade and liver function index. The proposed framework provides pathologists with a useful and reliable tool for hepatitis diagnosis.https://ieeexplore.ieee.org/document/10476647/Deep learningheterogeneous featuresinterface hepatitisIshak gradingstructural analysis |
| spellingShingle | Hung-Wen Tsai Chien-Yu Chiou Wei-Jong Yang Tsan-An Hsieh Cheng-Yi Chen Che-Wei Hsu Yih-Jyh Lin Min-En Hsieh Matthew M. Yeh Chin-Chun Chen Meng-Ru Shen Pau-Choo Chung Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features IEEE Open Journal of Engineering in Medicine and Biology Deep learning heterogeneous features interface hepatitis Ishak grading structural analysis |
| title | Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features |
| title_full | Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features |
| title_fullStr | Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features |
| title_full_unstemmed | Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features |
| title_short | Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features |
| title_sort | lymphocyte infiltrated periportal region detection with structurally refined deep portal segmentation and heterogeneous infiltration features |
| topic | Deep learning heterogeneous features interface hepatitis Ishak grading structural analysis |
| url | https://ieeexplore.ieee.org/document/10476647/ |
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