Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans

Jacob S. Leiby; Matthew E. Lee; Eun Kyung Choe; Dokyoon Kim

doi:10.1007/978-3-031-45676-3_27

Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans

Jacob S. Leiby, Matthew E. Lee, Eun Kyung Choe, Dokyoon Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Fatty liver disease is a prevalent condition with significant health implications and early detection may prevent adverse outcomes. In this study, we developed a data-driven classification framework using deep learning to classify fatty liver disease from unenhanced abdominal CT scans. The framework consisted of a two-stage pipeline: 3D liver segmentation and feature extraction, followed by a deep learning classifier. We compared the performance of different deep learning feature representations with volumetric liver attenuation, a hand-crafted radiomic feature. Additionally, we assessed the predictive capability of our classifier for the future occurrence of fatty liver disease. The deep learning models outperformed the liver attenuation model for baseline fatty liver classification, with an AUC of 0.90 versus 0.86, respectively. Furthermore, our classifier was better able to detect mild degrees of steatosis and demonstrated the ability to predict future occurrence of fatty liver disease.

Original language	English
Title of host publication	Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings
Editors	Xiaohuan Cao, Xi Ouyang, Xuanang Xu, Islem Rekik, Zhiming Cui
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	264-272
Number of pages	9
ISBN (Print)	9783031456756
DOIs	https://doi.org/10.1007/978-3-031-45676-3_27
State	Published - 2024
Event	14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 8 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14349 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023
Country/Territory	Canada
City	Vancouver
Period	8/10/23 → 8/10/23

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Computed Tomography
Deep Learning
Fatty Liver Disease

Access to Document

10.1007/978-3-031-45676-3_27

Cite this

Leiby, J. S., Lee, M. E., Choe, E. K., & Kim, D. (2024). Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans. In X. Cao, X. Ouyang, X. Xu, I. Rekik, & Z. Cui (Eds.), Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings (pp. 264-272). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14349 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-45676-3_27

Leiby, Jacob S. ; Lee, Matthew E. ; Choe, Eun Kyung et al. / Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans. Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings. editor / Xiaohuan Cao ; Xi Ouyang ; Xuanang Xu ; Islem Rekik ; Zhiming Cui. Springer Science and Business Media Deutschland GmbH, 2024. pp. 264-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Fatty liver disease is a prevalent condition with significant health implications and early detection may prevent adverse outcomes. In this study, we developed a data-driven classification framework using deep learning to classify fatty liver disease from unenhanced abdominal CT scans. The framework consisted of a two-stage pipeline: 3D liver segmentation and feature extraction, followed by a deep learning classifier. We compared the performance of different deep learning feature representations with volumetric liver attenuation, a hand-crafted radiomic feature. Additionally, we assessed the predictive capability of our classifier for the future occurrence of fatty liver disease. The deep learning models outperformed the liver attenuation model for baseline fatty liver classification, with an AUC of 0.90 versus 0.86, respectively. Furthermore, our classifier was better able to detect mild degrees of steatosis and demonstrated the ability to predict future occurrence of fatty liver disease.",

keywords = "Computed Tomography, Deep Learning, Fatty Liver Disease",

author = "Leiby, {Jacob S.} and Lee, {Matthew E.} and Choe, {Eun Kyung} and Dokyoon Kim",

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Leiby, JS, Lee, ME, Choe, EK & Kim, D 2024, Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans. in X Cao, X Ouyang, X Xu, I Rekik & Z Cui (eds), Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14349 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 264-272, 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, Vancouver, Canada, 8/10/23. https://doi.org/10.1007/978-3-031-45676-3_27

Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans. / Leiby, Jacob S.; Lee, Matthew E.; Choe, Eun Kyung et al.
Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings. ed. / Xiaohuan Cao; Xi Ouyang; Xuanang Xu; Islem Rekik; Zhiming Cui. Springer Science and Business Media Deutschland GmbH, 2024. p. 264-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14349 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans

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AU - Lee, Matthew E.

AU - Choe, Eun Kyung

AU - Kim, Dokyoon

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N2 - Fatty liver disease is a prevalent condition with significant health implications and early detection may prevent adverse outcomes. In this study, we developed a data-driven classification framework using deep learning to classify fatty liver disease from unenhanced abdominal CT scans. The framework consisted of a two-stage pipeline: 3D liver segmentation and feature extraction, followed by a deep learning classifier. We compared the performance of different deep learning feature representations with volumetric liver attenuation, a hand-crafted radiomic feature. Additionally, we assessed the predictive capability of our classifier for the future occurrence of fatty liver disease. The deep learning models outperformed the liver attenuation model for baseline fatty liver classification, with an AUC of 0.90 versus 0.86, respectively. Furthermore, our classifier was better able to detect mild degrees of steatosis and demonstrated the ability to predict future occurrence of fatty liver disease.

AB - Fatty liver disease is a prevalent condition with significant health implications and early detection may prevent adverse outcomes. In this study, we developed a data-driven classification framework using deep learning to classify fatty liver disease from unenhanced abdominal CT scans. The framework consisted of a two-stage pipeline: 3D liver segmentation and feature extraction, followed by a deep learning classifier. We compared the performance of different deep learning feature representations with volumetric liver attenuation, a hand-crafted radiomic feature. Additionally, we assessed the predictive capability of our classifier for the future occurrence of fatty liver disease. The deep learning models outperformed the liver attenuation model for baseline fatty liver classification, with an AUC of 0.90 versus 0.86, respectively. Furthermore, our classifier was better able to detect mild degrees of steatosis and demonstrated the ability to predict future occurrence of fatty liver disease.

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BT - Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings

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Leiby JS, Lee ME, Choe EK, Kim D. Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans. In Cao X, Ouyang X, Xu X, Rekik I, Cui Z, editors, Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 264-272. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-45676-3_27

Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans

Abstract

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Keywords

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