A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images

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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 < 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.

Original languageEnglish
Article number43
JournalCancer Imaging
Volume24
Issue number1
DOIs
StatePublished - Dec 2024

Keywords

  • Computed tomography
  • Deep learning
  • Detection
  • Hepatocellular carcinoma
  • Segmentation

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