Deep learning model for automatically segmenting prostate on large-field T2WI based on integrated 68Ga-prostate specific membrane antigen PET/MRI
10.13929/j.issn.1003-3289.2024.10.027
- VernacularTitle:深度学习模型自动分割基于一体化68Ga-前列腺特异性膜抗原PET/MRI大视野T2WI中的前列腺
- Author:
Guiyu LI
1
;
Wenhui MA
;
Junling WANG
;
Taoqi MA
;
Yunya WANG
;
Fei KANG
;
Weidong YANG
;
Jing WANG
Author Information
1. 空军军医大学第一附属医院核医学科,陕西西安 710032
- Keywords:
prostatic neoplasms;
deep learning;
positron-emission tomography;
magnetic resonance imaging
- From:
Chinese Journal of Medical Imaging Technology
2024;40(10):1588-1592
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of deep learning model for automatically segmenting prostate on large-field T2WI based on integrated 68Ga-prostate specific membrane antigen(PSMA)PET/MRI.Methods Ninety male patients with prostate tumors who underwent 68Ga-PSMA PET/MRI were retrospectively enrolled and divided into training set(n=72)and validation set(n=18)at the ratio of 4∶1.Models were established based on 3D SegResNet and 3D Unet deep learning neural networks,respectively.Taken physicians'manual segmentation results as reference standards,the performances of models for segmenting the peripheral zone(PZ)and central zone(CZ)+transition zone(TZ)of prostate on large-field T2WI were evaluated.Results In both training and validation sets,the Dice similarity coefficient(DSC)of 3D SegResNet deep learning model for segmenting prostate on T 2WI were both higher than that of 3D Unet model(both P＜0.05),the 95％Hausdorff distance(HD95)of SegR esNet deep learning model for segmenting prostate CZ+TZ was lower than that of 3D Unet model(both P＜0.05),while DSC and HD95 of these 2 models for segmenting prostate CZ+TZ were superior to PZ(all P＜0.05).Conclusion 3D SegResNet deep learning model could be used to automatically segment prostate on large-field T2WI based on integrated 68Ga-PSMA PET/MRI.
