Segmentation of Liver Tumor in CT Scan Using ResU-Net
Abstract
:1. Introduction
2. Related Work
3. Material and Method
3.1. Liver CT Scan Dataset
3.2. Image Pre-Processing
3.2.1. Hounsfield Windowing ()
3.2.2. Histogram Equalization
3.3. Data Augmentation
3.3.1. Reflection
3.3.2. Rotation
3.4. Image Normalization
3.5. ResU-Net Architecture for Liver CT Scan Segmentation
3.6. Loss Function
3.7. Evaluation Metric
3.7.1. Dice Similarity Coefficient ()
3.7.2. Accuracy
3.7.3. Specificity
3.7.4. Precision
3.7.5. Relative Volume Difference (RVD)
3.7.6. Volume Overlap Error ()
4. Result and Discussion
4.1. Experimental Set-Up
4.2. Liver Segmentation Results
4.3. Tumor Segmentation Results
4.4. Comparative Analysis
4.4.1. Segmentation of Liver
4.4.2. Segmentation of Liver Tumor
5. Conclusions
Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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HU Value for Body Organ | |
---|---|
Bone | 1000 |
Liver | 40 to 60 |
White Matter | 46 |
Grey Matter | 43 |
Blood | 40 |
Muscle | 10 to 40 |
Kidney | 30 |
Cerebrospinal | 15 |
Water | 0 |
Fat | −50 to −100 |
Air | −1000 |
S.No | Evaluation Metrics | ResU-Net Network |
---|---|---|
1 | DSC | 0.9781 |
2 | Accuracy | 0.991 |
3 | Precision | 0.982 |
4 | Specificity | 0.961 |
5 | VOE | 0.13 |
6 | RVD | 0.018 |
S.No | Evaluation Metrics | ResU-Net Network |
---|---|---|
1 | DSC | 0.893 |
2 | Accuracy | 0.97 |
3 | Precision | 0.950 |
4 | Specificity | 0.957 |
5 | VOE | 13.15 |
6 | RVD | 7.23 |
Approach | DSC | Accuracy | Precision | Specificity | VOE | RVD |
---|---|---|---|---|---|---|
Christ et al. [31] | 0.94 | - | - | - | 10.7 | −1.4 |
Rafiei et al. [36] | 0.935 | - | - | - | 56.47 | - |
Li et al. [14] | 0.80 | - | 0.826 | - | 29.04 | - |
Qiangguo Jin et al. [24] | 0.971 | - | - | - | 0.074 | 0.002 |
Sultan Almotairi et al. [26] | - | 0.988 | - | - | - | - |
Yodit Abebe Ayalew et al. [25] | 0.961 | 0.993 | - | - | - | |
Proposed ResU-Net | 0.9781 | 0.991 | 0.982 | 0.9618 | 0.13 | 0.018 |
Approach | DSC | Accuracy | Precision | Specificity | VOE | RVD |
---|---|---|---|---|---|---|
Christ et al. [13] | 0.823 | - | - | - | - | - |
Sun et al. [39] | - | - | - | - | 15.6 | 5.8 |
Wu et al. [38] | 0.83 | - | - | - | 29.04 | −2.20 |
Qiangguo Jin et al. [24] | 0.595 | - | - | - | 0.380 | −0.152 |
Han et al. [37] | 0.67 | - | - | - | - | 0.40 |
Yodit Abebe Ayalew et al. [25] | 0.74 | 0.995 | - | - | - | |
Proposed ResU-Net | 0.893 | 0.97 | 0.950 | 0.957 | 13.15 | 7.23 |
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Sabir, M.W.; Khan, Z.; Saad, N.M.; Khan, D.M.; Al-Khasawneh, M.A.; Perveen, K.; Qayyum, A.; Azhar Ali, S.S. Segmentation of Liver Tumor in CT Scan Using ResU-Net. Appl. Sci. 2022, 12, 8650. https://doi.org/10.3390/app12178650
Sabir MW, Khan Z, Saad NM, Khan DM, Al-Khasawneh MA, Perveen K, Qayyum A, Azhar Ali SS. Segmentation of Liver Tumor in CT Scan Using ResU-Net. Applied Sciences. 2022; 12(17):8650. https://doi.org/10.3390/app12178650
Chicago/Turabian StyleSabir, Muhammad Waheed, Zia Khan, Naufal M. Saad, Danish M. Khan, Mahmoud Ahmad Al-Khasawneh, Kiran Perveen, Abdul Qayyum, and Syed Saad Azhar Ali. 2022. "Segmentation of Liver Tumor in CT Scan Using ResU-Net" Applied Sciences 12, no. 17: 8650. https://doi.org/10.3390/app12178650
APA StyleSabir, M. W., Khan, Z., Saad, N. M., Khan, D. M., Al-Khasawneh, M. A., Perveen, K., Qayyum, A., & Azhar Ali, S. S. (2022). Segmentation of Liver Tumor in CT Scan Using ResU-Net. Applied Sciences, 12(17), 8650. https://doi.org/10.3390/app12178650