Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation
Issued Date
2023-06-01
Resource Type
ISSN
14248220
Scopus ID
2-s2.0-85164023641
Journal Title
Sensors
Volume
23
Issue
12
Rights Holder(s)
SCOPUS
Bibliographic Citation
Sensors Vol.23 No.12 (2023)
Suggested Citation
Apivanichkul K., Phasukkit P., Dankulchai P., Sittiwong W., Jitwatcharakomol T. Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation. Sensors Vol.23 No.12 (2023). doi:10.3390/s23125720 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/87864
Title
Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation
Author's Affiliation
Other Contributor(s)
Abstract
This research proposes augmenting cropped computed tomography (CT) slices with data attributes to enhance the performance of a deep-learning-based automatic left-femur segmentation scheme. The data attribute is the lying position for the left-femur model. In the study, the deep-learning-based automatic left-femur segmentation scheme was trained, validated, and tested using eight categories of CT input datasets for the left femur (F-I–F-VIII). The segmentation performance was assessed by Dice similarity coefficient (DSC) and intersection over union (IoU); and the similarity between the predicted 3D reconstruction images and ground-truth images was determined by spectral angle mapper (SAM) and structural similarity index measure (SSIM). The left-femur segmentation model achieved the highest DSC (88.25%) and IoU (80.85%) under category F-IV (using cropped and augmented CT input datasets with large feature coefficients), with an SAM and SSIM of 0.117–0.215 and 0.701–0.732. The novelty of this research lies in the use of attribute augmentation in medical image preprocessing to enhance the performance of the deep-learning-based automatic left-femur segmentation scheme.
