RT journal article
T1 Applicability of Deep Learning to Dynamically Identify the Different Organs of the Pelvic Floor in the Midsagittal Plane
A1 García-Mejido, José Antonio
A1 Solís Martín, David
A1 Martín Morán, Marina
A1 Fernández-Conde, C.
A1 Fernández Palacín, Fernando
A1 Sainz Bueno, José Antonio
A2 Estadística e Investigación Operativa
K1 Deep learning
K1 Pelvic foor
K1 Ultrasonography
K1 Convolutional neural network
K1 Artifcial intelligence
K1 Levator ani muscle
AB Introduction and Hypothesis: The objective was to create and validate the usefulness of a convolutional neural network (CNN) for identifying different organs of the pelvic floor in the midsagittal plane via dynamic ultrasound. Methods: This observational and prospective study included 110 patients. Transperineal ultrasound scans were performed by an expert sonographer of the pelvic floor. A video of each patient was made that captured the midsagittal plane of the pelvic floor at rest and the change in the pelvic structures during the Valsalva maneuver. After saving the captured videos, we manually labeled the different organs in each video. Three different architectures were tested—UNet, FPN, and LinkNet—to determine which CNN model best recognized anatomical structures. The best model was trained with the 86 cases for the number of epochs determined by the stop criterion via cross-validation. The Dice Similarity Index (DSI) was used for CNN validation. Results: Eighty-six patients were included to train the CNN and 24 to test the CNN. After applying the trained CNN to the 24 test videos, we did not observe any failed segmentation. In fact, we obtained a DSI of 0.79 (95% CI: 0.73 – 0.82) as the median of the 24 test videos. When we studied the organs independently, we observed differences in the DSI of each organ. The poorest DSIs were obtained in the bladder (0.71 [95% CI: 0.70 – 0.73]) and uterus (0.70 [95% CI: 0.68 – 0.74]), whereas the highest DSIs were obtained in the anus (0.81 [95% CI: 0.80 – 0.86]) and levator ani muscle (0.83 [95% CI: 0.82 – 0.83]). Conclusions: Our results show that it is possible to apply deep learning using a trained CNN to identify different pelvic floor organs in the midsagittal plane via dynamic ultrasound.
PB Springer Nature
SN 1433-3023
YR 2024
FD 2024
LK http://hdl.handle.net/10498/35571
UL http://hdl.handle.net/10498/35571
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026