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Volume 11
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10.3390/diagnostics11091599
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Article

Recognition Rate Advancement and Data Error Improvement of Pathology Cutting with H-DenseUNet for Hepatocellular Carcinoma Image

by
Wen-Fan Chen
1,†,
Hsin-You Ou
2,†,
Cheng-Tang Pan
3,
Chien-Chang Liao
2,
Wen Huang
3,
Han-Yu Lin
3,
Yu-Fan Cheng
2,* and
Chia-Po Wei
4,*
1
Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
2
Liver Transplantation Program and Departments of Diagnostic Radiology, Surgery Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833401, Taiwan
3
Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
4
Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
*
Authors to whom correspondence should be addressed.
†
These authors contribute equally.
Diagnostics 2021, 11(9), 1599; https://doi.org/10.3390/diagnostics11091599
Submission received: 4 August 2021 / Revised: 29 August 2021 / Accepted: 29 August 2021 / Published: 2 September 2021
(This article belongs to the Topic Artificial Intelligence in Healthcare)
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Abstract

Due to the fact that previous studies have rarely investigated the recognition rate discrepancy and pathology data error when applied to different databases, the purpose of this study is to investigate the improvement of recognition rate via deep learning-based liver lesion segmentation with the incorporation of hospital data. The recognition model used in this study is H-DenseUNet, which is applied to the segmentation of the liver and lesions, and a mixture of 2D/3D Hybrid-DenseUNet is used to reduce the recognition time and system memory requirements. Differences in recognition results were determined by comparing the training files of the standard LiTS competition data set with the training set after mixing in an additional 30 patients. The average error value of 9.6% was obtained by comparing the data discrepancy between the actual pathology data and the pathology data after the analysis of the identified images imported from Kaohsiung Chang Gung Memorial Hospital. The average error rate of the recognition output after mixing the LiTS database with hospital data for training was 1%. In the recognition part, the Dice coefficient was 0.52 after training 50 epochs using the standard LiTS database, while the Dice coefficient was increased to 0.61 after adding 30 hospital data to the training. After importing 3D Slice and ITK-Snap software, a 3D image of the lesion and liver segmentation can be developed. It is hoped that this method could be used to stimulate more research in addition to the general public standard database in the future, as well as to study the applicability of hospital data and improve the generality of the database.
Keywords: data comparison; deep learning; H-DenseUNet; lesion segmentation; liver segmentation; medical statistics; pathological data comparison; deep learning; H-DenseUNet; lesion segmentation; liver segmentation; medical statistics; pathological

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MDPI and ACS Style

Chen, W.-F.; Ou, H.-Y.; Pan, C.-T.; Liao, C.-C.; Huang, W.; Lin, H.-Y.; Cheng, Y.-F.; Wei, C.-P. Recognition Rate Advancement and Data Error Improvement of Pathology Cutting with H-DenseUNet for Hepatocellular Carcinoma Image. Diagnostics 2021, 11, 1599. https://doi.org/10.3390/diagnostics11091599

AMA Style

Chen W-F, Ou H-Y, Pan C-T, Liao C-C, Huang W, Lin H-Y, Cheng Y-F, Wei C-P. Recognition Rate Advancement and Data Error Improvement of Pathology Cutting with H-DenseUNet for Hepatocellular Carcinoma Image. Diagnostics. 2021; 11(9):1599. https://doi.org/10.3390/diagnostics11091599

Chicago/Turabian Style

Chen, Wen-Fan, Hsin-You Ou, Cheng-Tang Pan, Chien-Chang Liao, Wen Huang, Han-Yu Lin, Yu-Fan Cheng, and Chia-Po Wei. 2021. "Recognition Rate Advancement and Data Error Improvement of Pathology Cutting with H-DenseUNet for Hepatocellular Carcinoma Image" Diagnostics 11, no. 9: 1599. https://doi.org/10.3390/diagnostics11091599

APA Style

Chen, W.-F., Ou, H.-Y., Pan, C.-T., Liao, C.-C., Huang, W., Lin, H.-Y., Cheng, Y.-F., & Wei, C.-P. (2021). Recognition Rate Advancement and Data Error Improvement of Pathology Cutting with H-DenseUNet for Hepatocellular Carcinoma Image. Diagnostics, 11(9), 1599. https://doi.org/10.3390/diagnostics11091599

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