Deep learning promotes B-mode ultrasound screening for focal liver lesions

Yamada, Akira

doi:10.1016/j.ebiom.2020.102814

Cited by 2 publications

(1 citation statement)

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“…Meanwhile, although studies have reported AI models with considerable accuracy for detecting liver malignancy, the majority of these models were developed on static US images that were manually selected by radiologists [ 14 , 15 , 18–20 ]. However, in US images, the overall morphology of liver masses and their relationships with surrounding structures, which are critical for radiologists in making diagnosis, would most likely be lost [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames

Zheng

Liu

et al. 2022

Briefings in Bioinformatics

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Recent developments of deep learning methods have demonstrated their feasibility in liver malignancy diagnosis using ultrasound (US) images. However, most of these methods require manual selection and annotation of US images by radiologists, which limit their practical application. On the other hand, US videos provide more comprehensive morphological information about liver masses and their relationships with surrounding structures than US images, potentially leading to a more accurate diagnosis. Here, we developed a fully automated artificial intelligence (AI) pipeline to imitate the workflow of radiologists for detecting liver masses and diagnosing liver malignancy. In this pipeline, we designed an automated mass-guided strategy that used segmentation information to direct diagnostic models to focus on liver masses, thus increasing diagnostic accuracy. The diagnostic models based on US videos utilized bi-directional convolutional long short-term memory modules with an attention-boosted module to learn and fuse spatiotemporal information from consecutive video frames. Using a large-scale dataset of 50 063 US images and video frames from 11 468 patients, we developed and tested the AI pipeline and investigated its applications. A dataset of annotated US images is available at https://doi.org/10.5281/zenodo.7272660.

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