Back to Search
Start Over
Automatic segmentation of the uterus on MRI using a convolutional neural network
- Source :
- Computers in biology and medicine. 114
- Publication Year :
- 2019
-
Abstract
- Background This study was performed to evaluate the clinical feasibility of a U-net for fully automatic uterine segmentation on MRI by using images of major uterine disorders. Methods This study included 122 female patients (14 with uterine endometrial cancer, 15 with uterine cervical cancer, and 55 with uterine leiomyoma). U-net architecture optimized for our research was used for automatic segmentation. Three-fold cross-validation was performed for validation. The results of manual segmentation of the uterus by a radiologist on T2-weighted sagittal images were used as the gold standard. Dice similarity coefficient (DSC) and mean absolute distance (MAD) were used for quantitative evaluation of the automatic segmentation. Visual evaluation using a 4-point scale was performed by two radiologists. DSC, MAD, and the score of the visual evaluation were compared between uteruses with and without uterine disorders. Results The mean DSC of our model for all patients was 0.82. The mean DSCs for patients with and without uterine disorders were 0.84 and 0.78, respectively (p = 0.19). The mean MADs for patients with and without uterine disorders were 18.5 and 21.4 [pixels], respectively (p = 0.39). The scores of the visual evaluation were not significantly different between uteruses with and without uterine disorders. Conclusions Fully automatic uterine segmentation with our modified U-net was clinically feasible. The performance of the segmentation of our model was not influenced by the presence of uterine disorders.
- Subjects :
- 0301 basic medicine
Adult
medicine.medical_specialty
Uterus
Uterine Cervical Neoplasms
Health Informatics
Convolutional neural network
03 medical and health sciences
0302 clinical medicine
medicine
Image Processing, Computer-Assisted
Humans
Segmentation
Uterine leiomyoma
business.industry
Gold standard (test)
Middle Aged
Magnetic Resonance Imaging
Sagittal plane
Computer Science Applications
Uterine Disorder
030104 developmental biology
medicine.anatomical_structure
Automatic segmentation
Feasibility Studies
Female
Radiology
Neural Networks, Computer
business
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 18790534
- Volume :
- 114
- Database :
- OpenAIRE
- Journal :
- Computers in biology and medicine
- Accession number :
- edsair.doi.dedup.....1b543a1d33d5b909063596434c655f0c