Deep learning muscle segmentation model for CT images in DICOM format
Ian Schmidt, Elena Kotina, Pavel Buev
This work solves the problem of automatic segmentation of medical images in DICOM format using machine learning methods. A new developed tool is used in the form of a separate module for labeling medical data in the DICOM format. The trained model, proposed in the paper, can be useful in the tasks of muscle segmentation. One can apply it in different ways, but some of the most common include assessment of diseases related to muscles, and sarcopenia is one of them. The further applications of the muscle segmentation model may include examining various medical cases with patients, that tend to have muscle-related diseases. For instance, detecting cachexia may be one of the extensions of the model’s application field.
CYBERNETICS AND PHYSICS, Vol. 12, No. 3, 2023, 201-206
https://doi.org/10.35470/2226-4116-2023-12-3-201-206