Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the r...
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Published in | Scientific reports Vol. 11; no. 1; p. 22520 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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18.11.2021
Nature Publishing Group Nature Portfolio |
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Abstract | Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis. |
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AbstractList | Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis. Abstract Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis. Abstract Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis. |
ArticleNumber | 22520 |
Author | Thakur, Nishant Hwang, Gyoyeon Kim, Chulhong Yoon, Hongjoon Chong, Yosep Kim, Hyeongsub Lee, Eun Jung |
Author_xml | – sequence: 1 givenname: Hyeongsub surname: Kim fullname: Kim, Hyeongsub organization: Departments of Electrical Engineering, Creative IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Medical Device Innovation Center, and Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Deepnoid Inc – sequence: 2 givenname: Hongjoon surname: Yoon fullname: Yoon, Hongjoon organization: Deepnoid Inc – sequence: 3 givenname: Nishant surname: Thakur fullname: Thakur, Nishant organization: Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Uijeongbu St. Mary’s Hospital – sequence: 4 givenname: Gyoyeon surname: Hwang fullname: Hwang, Gyoyeon organization: Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Yeouido St. Mary’s Hospital – sequence: 5 givenname: Eun Jung surname: Lee fullname: Lee, Eun Jung organization: Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Yeouido St. Mary’s Hospital, Department of Pathology, Shinwon Medical Foundation – sequence: 6 givenname: Chulhong surname: Kim fullname: Kim, Chulhong email: chulhong@postech.edu organization: Departments of Electrical Engineering, Creative IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Medical Device Innovation Center, and Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH) – sequence: 7 givenname: Yosep surname: Chong fullname: Chong, Yosep email: ychong@catholic.ac.kr organization: Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Uijeongbu St. Mary’s Hospital |
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SubjectTerms | 631/114/1305 631/114/1564 639/705/117 Algorithms Biopsy Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - diagnostic imaging Computer Graphics Computers Deep Learning Humanities and Social Sciences Humans Image processing Image Processing, Computer-Assisted - methods Machine Learning multidisciplinary Neural networks Neural Networks, Computer Pattern recognition Predictive Value of Tests Principal Component Analysis Principal components analysis Probability Reproducibility of Results Science Science (multidisciplinary) Segmentation Software User-Computer Interface Wavelet Analysis Wavelet transforms |
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Title | Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain |
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