Segmentation of muscle tissue in computed tomography images at the level of the L3 vertebra
With the increasing routine workload on radiologists associated with the need to analyze large numbers of images, there is a need to automate part of the analysis process. Sarcopenia is a condition in which there is a loss of muscle mass. To diagnose sarcopenia, computed tomography is most often u...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
2024-02-01
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| Series: | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| Subjects: | |
| Online Access: | https://ntv.ifmo.ru/file/article/22599.pdf |
| Summary: | With the increasing routine workload on radiologists associated with the need to analyze large numbers of images, there
is a need to automate part of the analysis process. Sarcopenia is a condition in which there is a loss of muscle mass. To
diagnose sarcopenia, computed tomography is most often used, from the images of which the volume of muscle tissue
can be assessed. The first stage of the analysis is its contouring, which is performed manually, takes a long time and is
not always performed with sufficient quality affecting the accuracy of estimates and, as a result, the patient’s treatment
plan. The subject of the study is the use of computer vision approaches for accurate segmentation of muscle tissue
from computed tomography images for the purpose of sarcometry. The purpose of the study is to develop an approach
to solving the problem of segmentation of collected and annotated images. An approach is presented that includes
the stages of image pre-processing, segmentation using neural networks of the U-Net family, and post-processing. In
total, 63 different configurations of the approach are considered, which differ in terms of data supplied to the input
models and model architectures. The influence of the proposed method of post-processing the resulting binary masks
on the segmentation accuracy is also evaluated. The approach, which includes pre-processing with table masking and
anisotropic diffusion filtering, segmentation with an Inception U-Net architecture model, and post-processing based
on contour analysis, achieves a Dice similarity coefficient of 0.9379 and Intersection over Union of 0.8824. Nine
other configurations, the experimental results for which are reflected in the article, also demonstrated high values of
these metrics (in the ranges of 0.9356–0.9374 and 0.8794–0.8822, respectively). The approach proposed in the article
based on preprocessed three-channel images allows us to achieve metrics of 0.9364 and 0.8802, respectively, using the
lightweight U-Net segmentation model. In accordance with the described approach, a software module was implemented
in Python. The results of the study confirm the feasibility of using computer vision to assess muscle tissue parameters.
The developed module can be used to reduce the routine workload on radiologists. |
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| ISSN: | 2226-1494 2500-0373 |