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1.
Comput Biol Med ; 153: 106478, 2023 02.
Article in English | MEDLINE | ID: mdl-36603437

ABSTRACT

Liver Ultrasound (US) or sonography is popularly used because of its real-time output, low-cost, ease-of-use, portability, and non-invasive nature. Segmentation of real-time liver US is essential for diagnosing and analyzing liver conditions (e.g., hepatocellular carcinoma (HCC)), assisting the surgeons/radiologists in therapeutic procedures. In this paper, we propose a method using a modified Pyramid Scene Parsing (PSP) module in tuned neural network backbones to achieve real-time segmentation without compromising the segmentation accuracy. Considering widespread noise in US data and its impact on outcomes, we study the impact of pre-processing and the influence of loss functions on segmentation performance. We have tested our method after annotating a publicly available US dataset containing 2400 images of 8 healthy volunteers (link to the annotated dataset is provided); the results show that the Dense-PSP-UNet model achieves a high Dice coefficient of 0.913±0.024 while delivering a real-time performance of 37 frames per second (FPS).


Subject(s)
Carcinoma, Hepatocellular , Liver Neoplasms , Humans , Carcinoma, Hepatocellular/diagnostic imaging , Liver Neoplasms/diagnostic imaging , Ultrasonography , Neural Networks, Computer , Image Processing, Computer-Assisted
3.
Sci Rep ; 12(1): 14153, 2022 08 19.
Article in English | MEDLINE | ID: mdl-35986015

ABSTRACT

Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.


Subject(s)
Image Processing, Computer-Assisted , Liver Neoplasms , Humans , Image Processing, Computer-Assisted/methods , Liver Neoplasms/diagnostic imaging , Liver Neoplasms/pathology , Neural Networks, Computer , Tomography, X-Ray Computed/methods
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