TD-Net: A Hybrid End-to-End Network for Automatic Liver Tumor Segmentation From CT Images

Di, Shuanhu; Zhao, Yuqian; Liao, Miao; Zhang, Fan; Li, Xiong

doi:10.1109/jbhi.2022.3181974

Cited by 22 publications

(1 citation statement)

References 37 publications

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“…This is primarily because of the uncertainty of anatomical location and low image resolution of plain-scan CT. The current obstacles and challenges in developing CT image-guided, online ART–SBRT for sHCC involve identifying and aligning the tumor target area and automatic segmentation. − …”

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confidence: 99%

Charged Gold Nanoparticles for Target Identification–Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma

Lai,

Luo,

Liu

et al. 2024

Nano Lett.

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Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification−alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART.

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confidence: 99%