Deep learning muscle segmentation model for CT images in DICOM format

Standard

Deep learning muscle segmentation model for CT images in DICOM format. / Шмидт, Ян Александрович ; Котина, Елена Дмитриевна ; Буев, Павел Иванович.

In: Cybernetics and Physics, Vol. 12, No. 3, 30.11.2023, p. 201-206.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{46064dc25ebd4c13ae1da12e6216729b,

title = "Deep learning muscle segmentation model for CT images in DICOM format",

abstract = "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{\textquoteright}s application field.",

keywords = "DICOM, Medical imaging, computer tomography (CT), machine learning, muscle segmentation",

author = "Шмидт, {Ян Александрович} and Котина, {Елена Дмитриевна} and Буев, {Павел Иванович}",

year = "2023",

month = nov,

day = "30",

doi = "10.35470/2226-4116-2023-12-3-201-206",

language = "English",

volume = "12",

pages = "201--206",

journal = "Cybernetics and Physics",

issn = "2223-7038",

publisher = "IPACS",

number = "3",

}

RIS

TY - JOUR

T1 - Deep learning muscle segmentation model for CT images in DICOM format

AU - Шмидт, Ян Александрович

AU - Котина, Елена Дмитриевна

AU - Буев, Павел Иванович

PY - 2023/11/30

Y1 - 2023/11/30

N2 - 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.

AB - 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.

KW - DICOM

KW - Medical imaging

KW - computer tomography (CT)

KW - machine learning

KW - muscle segmentation

UR - https://www.mendeley.com/catalogue/fd9a9b99-1614-367f-9e70-95e2f03caa8a/

U2 - 10.35470/2226-4116-2023-12-3-201-206

DO - 10.35470/2226-4116-2023-12-3-201-206

M3 - Article

VL - 12

SP - 201

EP - 206

JO - Cybernetics and Physics

JF - Cybernetics and Physics

SN - 2223-7038

IS - 3

ER -

ID: 114504062