Optimization-Incorporated Deep Learning Strategy to Automate L3 Slice Detection and Abdominal Segmentation in Computed Tomography
Abstract
1. Introduction
- To address the imbalance of target classes, an optimization process is proposed in which the augmentation ratio and class weight adjustments are considered as correction design variables (CDVs), and the objective function is defined based on the performance of the training models.
- The proposed optimization-integrated deep learning strategy is validated using various state-of-the-art deep learning techniques.
- The proposed strategy is applied to human CT images to extract the L3 slice and segment abdominal tissues.
2. L3 Slice Detection and Abdominal Segmentation Strategy
2.1. Deep Learning Architectures
2.2. Optimization Approach
3. Dataset
4. Model Implementation
4.1. Preprocessing
4.2. Implementation of L3 Slice Detection Model with Optimization
4.3. Implementation of Abdominal Segmentation Model with Optimization
5. Performance Evaluation
6. Results
7. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Type | Range |
---|---|---|
L2Regularization | Logarithmic (continuous) | [0.0001, 0.01] |
InitialLearningRate | Logarithmic (continuous) | [0.0001, 0.01] |
Batchsize | Integer (discrete) | [10, 32] |
GradientThreshold | Integer (discrete) | [1, 6] |
Epoch | Integer (discrete) | [5, 20] |
Momentum | Real (continuous) | [0.7, 0.99] |
All Patients (n = 150) | ||
---|---|---|
Disease | Prostate cancer, n (%) Bladder cancer, n (%) | 104 (69.3) 46 (30.7) |
Sex | Male, n (%), female, n (%) | 142 (94.7), 8 (5.3) |
Age | Median age, yr (IQR) | 67.5 (62.2–73.0) |
BMI | Median BMI, kg/m2 (IQR) | 24.5 (22.6–26.2) |
Height | Median height, cm (IQR) | 165.0 (161.1–168.7) |
Weight | Median weight, kg (IQR) | 66.4 (60.1–72.3) |
DM | n (%) | 37 (24.7) |
HTN | n (%) | 67 (44.7) |
Tissue | Jaccard Score | Dice Coefficient | Sensitivity | Specificity | MSD | ||
---|---|---|---|---|---|---|---|
Standard Unet | Without CDVs | SM | 0.908 ± 0.006 | 0.950 ± 0.004 | 0.944 ± 0.003 | 0.994 ± 0.001 | 1.027 ± 0.210 |
VAT | 0.871 ± 0.005 | 0.906 ± 0.004 | 0.936 ± 0.011 | 0.989 ± 0.002 | 1.721 ± 0.261 | ||
SAT | 0.912 ± 0.004 | 0.945 ± 0.003 | 0.965 ± 0.009 | 0.995 ± 0.001 | 0.812 ± 0.188 | ||
Abdomen | 0.963 ± 0.001 | 0.981 ± 0.001 | 0.976 ± 0.004 | 0.957 ± 0.006 | 0.310 ± 0.121 | ||
With CDVs | SM | 0.945 ± 0.001 | 0.971 ± 0.001 | 0.981 ± 0.003 | 0.996 ± 0.001 | 0.618 ± 0.234 | |
VAT | 0.898 ± 0.003 | 0.924 ± 0.002 | 0.963 ± 0.007 | 0.996 ± 0.001 | 1.287 ± 0.168 | ||
SAT | 0.960 ± 0.001 | 0.976 ± 0.001 | 0.980 ± 0.004 | 0.998 ± 0.001 | 0.354 ± 0.153 | ||
Abdomen | 0.974 ± 0.001 | 0.987 ± 0.001 | 0.988 ± 0.001 | 0.976 ± 0.002 | 0.312 ± 0.134 | ||
Swin-Unet | Without CDVs | SM | 0.936 ± 0.005 | 0.967 ± 0.003 | 0.969 ± 0.003 | 0.992 ± 0.001 | 0.754 ± 0.093 |
VAT | 0.875 ± 0.012 | 0.933 ± 0.007 | 0.922 ± 0.006 | 0.994 ± 0.001 | 0.692 ± 0.188 | ||
SAT | 0.899 ± 0.008 | 0.947 ± 0.004 | 0.953 ± 0.006 | 0.994 ± 0.001 | 0.582 ± 0.177 | ||
Abdomen | 0.964 ± 0.002 | 0.981 ± 0.001 | 0.982 ± 0.001 | 0.951 ± 0.004 | 0.375 ± 0.133 | ||
With CDVs | SM | 0.952 ± 0.004 | 0.975 ± 0.002 | 0.981 ± 0.003 | 0.977 ± 0.001 | 0.588 ± 0.080 | |
VAT | 0.903 ± 0.012 | 0.949 ± 0.006 | 0.973 ± 0.002 | 0.995 ± 0.001 | 0.442 ± 0.145 | ||
SAT | 0.962 ± 0.005 | 0.975 ± 0.003 | 0.974 ± 0.003 | 0.992 ± 0.001 | 0.404 ± 0.126 | ||
Abdomen | 0.982 ± 0.001 | 0.986 ± 0.001 | 0.981 ± 0.001 | 0.997 ± 0.001 | 0.279 ± 0.113 | ||
SegFormer | Without CDVs | SM | 0.942 ± 0.007 | 0.970 ± 0.004 | 0.984 ± 0.001 | 0.996 ± 0.001 | 0.325 ± 0.115 |
VAT | 0.821 ± 0.018 | 0.901 ± 0.011 | 0.930 ± 0.008 | 0.986 ± 0.001 | 0.847 ± 0.171 | ||
SAT | 0.914 ± 0.006 | 0.955 ± 0.003 | 0.975 ± 0.002 | 0.992 ± 0.001 | 0.585 ± 0.138 | ||
Abdomen | 0.960 ± 0.002 | 0.979 ± 0.001 | 0.971 ± 0.001 | 0.970 ± 0.003 | 0.385 ± 0.143 | ||
With CDVs | SM | 0.957 ± 0.002 | 0.978 ± 0.001 | 0.989 ± 0.003 | 0.987 ± 0.001 | 0.548 ± 0.193 | |
VAT | 0.907 ± 0.008 | 0.951 ± 0.002 | 0.975 ± 0.001 | 0.992 ± 0.001 | 0.967 ± 0.316 | ||
SAT | 0.969 ± 0.002 | 0.975 ± 0.001 | 0.975 ± 0.001 | 0.985 ± 0.001 | 0.416 ± 0.228 | ||
Abdomen | 0.986 ± 0.001 | 0.987 ± 0.001 | 0.992 ± 0.001 | 0.994 ± 0.001 | 0.376 ± 0.141 |
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Chae, S.; Chae, S.; Kang, T.G.; Kim, S.J.; Choi, A. Optimization-Incorporated Deep Learning Strategy to Automate L3 Slice Detection and Abdominal Segmentation in Computed Tomography. Bioengineering 2025, 12, 367. https://doi.org/10.3390/bioengineering12040367
Chae S, Chae S, Kang TG, Kim SJ, Choi A. Optimization-Incorporated Deep Learning Strategy to Automate L3 Slice Detection and Abdominal Segmentation in Computed Tomography. Bioengineering. 2025; 12(4):367. https://doi.org/10.3390/bioengineering12040367
Chicago/Turabian StyleChae, Seungheon, Seongwon Chae, Tae Geon Kang, Sung Jin Kim, and Ahnryul Choi. 2025. "Optimization-Incorporated Deep Learning Strategy to Automate L3 Slice Detection and Abdominal Segmentation in Computed Tomography" Bioengineering 12, no. 4: 367. https://doi.org/10.3390/bioengineering12040367
APA StyleChae, S., Chae, S., Kang, T. G., Kim, S. J., & Choi, A. (2025). Optimization-Incorporated Deep Learning Strategy to Automate L3 Slice Detection and Abdominal Segmentation in Computed Tomography. Bioengineering, 12(4), 367. https://doi.org/10.3390/bioengineering12040367