A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images
© 2024. The Author(s)..
BACKGROUND: Automatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from dynamic CT images.
METHODS: Dynamic CT images of 595 patients with HCC were used. Tumors in dynamic CT images were labeled by radiologists. Patients were randomly divided into training, validation and test sets in a ratio of 5:2:3, respectively. We developed a hierarchical fusion strategy of deep learning networks (HFS-Net). Global dice, sensitivity, precision and F1-score were used to measure performance of the HFS-Net model.
RESULTS: The 2D DenseU-Net using dynamic CT images was more effective for segmenting small tumors, whereas the 2D U-Net using portal venous phase images was more effective for segmenting large tumors. The HFS-Net model performed better, compared with the single-strategy deep learning models in segmenting small and large tumors. In the test set, the HFS-Net model achieved good performance in identifying HCC on dynamic CT images with global dice of 82.8%. The overall sensitivity, precision and F1-score were 84.3%, 75.5% and 79.6% per slice, respectively, and 92.2%, 93.2% and 92.7% per patient, respectively. The sensitivity in tumors < 2 cm, 2-3, 3-5 cm and > 5 cm were 72.7%, 92.9%, 94.2% and 100% per patient, respectively.
CONCLUSIONS: The HFS-Net model achieved good performance in the detection and segmentation of HCC from dynamic CT images, which may support radiologic diagnosis and facilitate automatic radiomics analysis.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
Cancer imaging : the official publication of the International Cancer Imaging Society - 24(2024), 1 vom: 26. März, Seite 43 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lee, I-Cheng [VerfasserIn] |
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| Links: |
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| Themen: |
Computed tomography |
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| Anmerkungen: |
Date Completed 28.03.2024 Date Revised 11.04.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s40644-024-00686-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370220595 |
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| 245 | 1 | 2 | |a A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a BACKGROUND: Automatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from dynamic CT images | ||
| 520 | |a METHODS: Dynamic CT images of 595 patients with HCC were used. Tumors in dynamic CT images were labeled by radiologists. Patients were randomly divided into training, validation and test sets in a ratio of 5:2:3, respectively. We developed a hierarchical fusion strategy of deep learning networks (HFS-Net). Global dice, sensitivity, precision and F1-score were used to measure performance of the HFS-Net model | ||
| 520 | |a RESULTS: The 2D DenseU-Net using dynamic CT images was more effective for segmenting small tumors, whereas the 2D U-Net using portal venous phase images was more effective for segmenting large tumors. The HFS-Net model performed better, compared with the single-strategy deep learning models in segmenting small and large tumors. In the test set, the HFS-Net model achieved good performance in identifying HCC on dynamic CT images with global dice of 82.8%. The overall sensitivity, precision and F1-score were 84.3%, 75.5% and 79.6% per slice, respectively, and 92.2%, 93.2% and 92.7% per patient, respectively. The sensitivity in tumors < 2 cm, 2-3, 3-5 cm and > 5 cm were 72.7%, 92.9%, 94.2% and 100% per patient, respectively | ||
| 520 | |a CONCLUSIONS: The HFS-Net model achieved good performance in the detection and segmentation of HCC from dynamic CT images, which may support radiologic diagnosis and facilitate automatic radiomics analysis | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Detection | |
| 650 | 4 | |a Hepatocellular carcinoma | |
| 650 | 4 | |a Segmentation | |
| 700 | 1 | |a Tsai, Yung-Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Yen-Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Ting-Chun |e verfasserin |4 aut | |
| 700 | 1 | |a Yen, Chia-Heng |e verfasserin |4 aut | |
| 700 | 1 | |a Chiu, Nai-Chi |e verfasserin |4 aut | |
| 700 | 1 | |a Hwang, Hsuen-En |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Chien-An |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Jia-Guan |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Rheun-Chuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chao, Yee |e verfasserin |4 aut | |
| 700 | 1 | |a Ho, Shinn-Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yi-Hsiang |e verfasserin |4 aut | |
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