Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning.

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 (J

SUBMITTER: Ryu H 

PROVIDER: S-EPMC8523410 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature