A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images.
Cancer Imaging
; 24(1): 43, 2024 Mar 26.
Article
em En
| MEDLINE
| ID: mdl-38532511
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 523, 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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Carcinoma Hepatocelular
/
Aprendizado Profundo
/
Neoplasias Hepáticas
Limite:
Humans
Idioma:
En
Revista:
Cancer Imaging
Assunto da revista:
DIAGNOSTICO POR IMAGEM
/
NEOPLASIAS
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Taiwan