Measurement of whole-body lymphoma segmentation and prognostic indicators based on 3D UNET network

Qing Wang; Bin Sun; Mingxin Zhao; Kai Sun; Ruihong Wang

doi:10.1117/12.3036613

13 July 2024 Measurement of whole-body lymphoma segmentation and prognostic indicators based on 3D UNET network

Qing Wang, Bin Sun, Mingxin Zhao, Kai Sun, Ruihong Wang

Proceedings Volume 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024); 132082B (2024) https://doi.org/10.1117/12.3036613
Event: 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 2024, Nanchang, China

Abstract

Nowadays, lymphoma has become a tumor type characterized by high morbidity and a low survival rate. Lymph spreads throughout the body, and the tumor's volume and size are irregular, making it cumbersome for doctors to diagnose. At the same time, artificial intelligence is developing rapidly, and its application in the medical field is becoming more and more extensive, so it is possible to detect lymphoma and assist doctors' work by using artificial intelligence. The purpose of this paper is to automatically complete the detection of lymphoma segmentation and the calculation of prognostic indicators with the help of artificial intelligence, and to assist doctors in determining a good treatment plan, as well as assessing the metabolic information of the tumor, predicting the progression of the disease, and reducing the workload of doctors. The experiment used the NIFTI data of 45 patients from Inner Mongolia Autonomous Region People's Hospital, and the data were labeled under the guidance of doctors. The data were divided into training and test sets at a 7:1 ratio, and 8-fold cross-validation was performed. The segmentation of lymphoma was performed by a 3D UNET network, and three prognostic indicators, tumor metabolic volume TMTV, maximum diffusion distance Dmax, and total lesion glycolysis ratio TLG, were measured based on segmentation. In the experiment, the training DSC was 90.50%, and the validation DSC was 86.80% after 350 rounds of Epoch training. The researchers measured the three prognostic indicators simultaneously. The experiment proved the feasibility of using the method of artificial intelligence for the detection and evaluation of lymphoma.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qing Wang, Bin Sun, Mingxin Zhao, Kai Sun, and Ruihong Wang "Measurement of whole-body lymphoma segmentation and prognostic indicators based on 3D UNET network", Proc. SPIE 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 132082B (13 July 2024); https://doi.org/10.1117/12.3036613

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KEYWORDS

Lymphoma

Image segmentation

Positron emission tomography

Education and training

Tumors

Voxels

3D image processing

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