دورية أكاديمية
A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images
| العنوان: | A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images |
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| المؤلفون: | I-Cheng Lee, Yung-Ping Tsai, Yen-Cheng Lin, Ting-Chun Chen, Chia-Heng Yen, Nai-Chi Chiu, Hsuen-En Hwang, Chien-An Liu, Jia-Guan Huang, Rheun-Chuan Lee, Yee Chao, Shinn-Ying Ho, Yi-Hsiang Huang |
| المصدر: | Cancer Imaging, Vol 24, Iss 1, Pp 1-10 (2024) |
| بيانات النشر: | BMC, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medical physics. Medical radiology. Nuclear medicine LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| مصطلحات موضوعية: | Hepatocellular carcinoma, Deep learning, Segmentation, Detection, Computed tomography, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| الوصف: | Abstract 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 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1470-7330 |
| Relation: | https://doaj.org/toc/1470-7330 |
| DOI: | 10.1186/s40644-024-00686-8 |
| URL الوصول: | https://doaj.org/article/a92c0700a21943ae84df10a7de856154 |
| رقم الأكسشن: | edsdoj.92c0700a21943ae84df10a7de856154 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 14707330 |
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| DOI: | 10.1186/s40644-024-00686-8 |