A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images
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 - 24(2024), 1 vom: 26. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lee, I-Cheng [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Computed tomography |
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| Anmerkungen: |
© The Author(s) 2024 |
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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): |
SPR055312497 |
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| 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. 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. | ||
| 650 | 4 | |a Hepatocellular carcinoma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Segmentation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Computed tomography |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Tsai, Yung-Ping |4 aut | |
| 700 | 1 | |a Lin, Yen-Cheng |4 aut | |
| 700 | 1 | |a Chen, Ting-Chun |4 aut | |
| 700 | 1 | |a Yen, Chia-Heng |4 aut | |
| 700 | 1 | |a Chiu, Nai-Chi |4 aut | |
| 700 | 1 | |a Hwang, Hsuen-En |4 aut | |
| 700 | 1 | |a Liu, Chien-An |4 aut | |
| 700 | 1 | |a Huang, Jia-Guan |4 aut | |
| 700 | 1 | |a Lee, Rheun-Chuan |4 aut | |
| 700 | 1 | |a Chao, Yee |4 aut | |
| 700 | 1 | |a Ho, Shinn-Ying |4 aut | |
| 700 | 1 | |a Huang, Yi-Hsiang |0 (orcid)0000-0001-5241-5425 |4 aut | |
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