Back to Search
Start Over
Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning
- Source :
- European Radiology
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Objectives To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images. Methods In total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). Results We achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947. Conclusions The proposed joint system exhibited fair performance compared to segmentation only and classification only systems. Key Points • The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination. • The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems. • The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.
- Subjects :
- medicine.medical_specialty
Carcinoma, Hepatocellular
Jaccard index
Convolutional neural network
030218 nuclear medicine & medical imaging
03 medical and health sciences
Deep Learning
0302 clinical medicine
Humans
Medicine
Radiology, Nuclear Medicine and imaging
Segmentation
Ultrasonography
Neuroradiology
Receiver operating characteristic
business.industry
Deep learning
Liver Neoplasms
Pattern recognition
General Medicine
Euclidean distance
Imaging Informatics and Artificial Intelligence
Liver
030220 oncology & carcinogenesis
Neural Networks, Computer
Hepatic Cyst
Artificial intelligence
Radiology
business
Subjects
Details
- ISSN :
- 14321084 and 09387994
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- European Radiology
- Accession number :
- edsair.doi.dedup.....c91a3ab89b714edc286092c3831e5d81