Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach
Abstract
:1. Introduction
1.1. Related Literature
1.1.1. Detection & Diagnosis Software Tools
1.1.2. Research Utilizing ML/DL-Based Methodologies
1.1.3. Research Gap
2. Materials and Methods
2.1. Data Annotation
2.2. Statistical Analysis of the Dataset
2.3. Operationl Framework
2.4. Experemintal Setup
3. Results
3.1. Baseline Landmarks Detection
3.2. Pipeline Landmark Detection
3.2.1. First Stage: Instance Segmentation
3.2.2. Second Stage: Landmark Detection
3.2.3. Performance Metrics
4. Discussion
5. Conclusions
- Fully annotating an open-source dataset [7] for instance segmentation and landmarks detection, which was published solely for binary classification task.
- Building an efficient deep-learning landmarks detection model with error margin of 2.402 ± 1.963° for the acetabular index angle measurement.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Ref. | Dataset Size | Methods | Performance | Limitation |
---|---|---|---|---|
[18] | 400 | Random Forest Regression Voting Model | Point-to-point error: 2.72 mm | Lack of implementation details for reproducibility. |
[20] | 9369 | Multi-Task Hourglass Network with Encoder-Decoder | Landmarks Pixel Error: 4.67 ± 5.3 ACI Angle Error: 2.776 ± 2.381° | Extreme diversity of bone morphology in these X-ray images due to large age range |
[21] | 11,574 | Mask R-CNN built on FPN and ResNet101 | AI measurement: 40.36 ± 4.15° Surgeons’ measurement: 39.59 ± 6.87° | Solely assessing sharp angle |
[22] | 1398 | Mask R-CNN, HRNet, ResNet50 for segmentation, keypoint detection, and classification respectively. | Landmarks Mean Euclidean distance error: 4.653 | Lack of integration with clinical practice |
[23] | 321 | Mask R-CNN for segmentation | Base Angles Difference: 1.81° | Solely used Ultrasound scans Lack of implementation details for keypoint detection model |
[24] | 10,219 | FR-DDH network with ResNet-101 for feature map and spatial information extraction | Bland–Altman 95% limits of agreement for AcI: −4.733° | Extreme diversity of bone morphology in X-ray images for wide age range Lack of integration with clinical practice |
[28] | 10,000 | CNN—Pyramid Non-local UNet (PN-UNet) | Average point-to-point error: 9.286 µm | Local dataset with manual annotations |
Five Number Summary | ||
---|---|---|
DDH Angle | Minimum | 16.442 |
Q1 | 24.869 | |
Median | 28.931 | |
Q3 | 47.278 | |
Maximum | 61.932 | |
IQR | 22.409 | |
Normal Angle | Minimum | 3.498 |
Q1 | 16.968 | |
Median | 19.947 | |
Q3 | 22.976 | |
Maximum | 33.479 | |
IQR | 6.008 |
Model Hyperparameters | |
---|---|
RPN Batch Size | 256 |
Roi Heads Batch Size | 128 |
Base Learning Rate | 0.00075 |
Maximum Iteration | 2500 |
Weight Decay | 0.0001 |
Batch Size | 4 |
Model | RMSE | AcI Error | Pixel Error |
---|---|---|---|
Baseline ResNet50 | 7.108 | 4.3705 ± 3.565 | 7.141 ± 6.7355 |
Pipelined ResNet50 | 3.408 | 3.3275 ± 2.94 | 4.266 ± 3.545 |
Baseline Keypoint RCNN | 2.9 | 2.955 ± 2.51 | 3.309 ± 2.410 |
Pipelined Keypoint RCNN | 2.7 | 2.402 ± 1.963 | 2.862 ± 2.392 |
Pipelined Keypoint RCNN with Binary Mask Segmentation: Pixel Error | ||||
---|---|---|---|---|
Mean | Std | Median | Mean ± Std | |
Landmark RU | 2.582 | 1.908 | 2.204 | 2.582 ± 1.908 |
Landmark RD | 3.287 | 2.947 | 2.35 | 3.287 ± 2.947 |
Landmark LU | 3.584 | 3.107 | 2.656 | 3.584 ± 3.107 |
Landmark LD | 1.993 | 1.607 | 1.552 | 1.993 ± 1.607 |
Avg | 2.862 | 2.392 | 2.190 | 2.862 ± 2.392 |
Pipelined Keypoint RCNN with Binary Mask Segmentation: AcI Measurement Error Rate | ||||
---|---|---|---|---|
Mean | Std | Median | Mean ± Std | |
Left | 2.127 | 1.732 | 1.996 | 2.127 ± 1.732 |
Right | 2.676 | 2.194 | 2.37 | 2.676 ± 2.194 |
Avg | 2.402 | 1.963 | 2.183 | 2.402 ± 1.963 |
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Jan, F.; Rahman, A.; Busaleh, R.; Alwarthan, H.; Aljaser, S.; Al-Towailib, S.; Alshammari, S.; Alhindi, K.R.; Almogbil, A.; Bubshait, D.A.; et al. Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach. J. Imaging 2023, 9, 242. https://doi.org/10.3390/jimaging9110242
Jan F, Rahman A, Busaleh R, Alwarthan H, Aljaser S, Al-Towailib S, Alshammari S, Alhindi KR, Almogbil A, Bubshait DA, et al. Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach. Journal of Imaging. 2023; 9(11):242. https://doi.org/10.3390/jimaging9110242
Chicago/Turabian StyleJan, Farmanullah, Atta Rahman, Roaa Busaleh, Haya Alwarthan, Samar Aljaser, Sukainah Al-Towailib, Safiyah Alshammari, Khadeejah Rasheed Alhindi, Asrar Almogbil, Dalal A. Bubshait, and et al. 2023. "Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach" Journal of Imaging 9, no. 11: 242. https://doi.org/10.3390/jimaging9110242