학술논문

3D reconstruction of the uterus and automatic segmentation of the uterine cavity on 3D magnetic resonance imaging: A preliminary study
Document Type
article
Source
Heliyon, Vol 10, Iss 1, Pp e23558- (2024)
Subject
Deep learning
Magnetic resonance imaging
Septate uterus
3D reconstruction
Automatic segmentation
Science (General)
Q1-390
Social sciences (General)
H1-99
Language
English
ISSN
2405-8440
Abstract
Purpose: To determine the differences in 3D shape features between septate uterus (SU) and normal uterus and to train a network to automatically delineate uterine cavity on 3D magnetic resonance imaging (MRI). Methods: A total of 43 patients (22 cases of partial septate uterus and 21 cases of complete septate uterus) were included in the experimental group. Nine volunteers were recruited as a control group. The uterine cavity (UC), myometrium (UM), and cervical canal of the uterus were segmented manually using ITK-SNAP software. The three-dimensional shape features of the UC and UM were extracted by using PyRadiomics. The recurrent saliency transformation network (RSTN) method was used to segment the UC. Results: The values of four 3D shape features were significantly lower in the control group than in the partial septate group and the complete septate group, while the values of two features were significantly higher (p