Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients
Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Datasets
2.1.1. Internal Training and Test Set
2.1.2. External Test Set
2.2. Ground Truth Delineations
2.3. Deep Learning Models
2.3.1. nnU-Net for Deep Learning Framework
Model Selection
2.3.2. SynthSeg
2.4. Evaluation
2.4.1. Segmentation Metrics
2.4.2. Local Evaluation of Ventricle Segmentation
2.4.3. Clinical Evaluation Ventricle Segmentation
2.5. Experiments and Statistical Analysis
3. Results
3.1. Ventricle Segmentation Results
3.2. Local Evaluation of Ventricle Segmentations
3.3. Clinical Evaluation Ventricle Segmentation Results
3.4. PVS Segmentation Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Internal Training Set (n = 78) | Internal Test Set (n = 18) | External Test Set (n = 18) | |
Sex | |||
| 33 (42%) | 11 (61.1%) | 14 (77.8%) |
| 45 (58%) | 7 (38.9%) | 4 (22.2%) |
Median age (IQR); range | 54.5 (40.8–68); 23–83 | 57.0 (41.5–63.3) 25–74 | 43.1 (33.0–51.1); 29–59 |
| |||
| 15 (19%) | 2 (11.1%) | 0 (0.0%) |
| 9 (11%) | 1 (5.6%) | 0 (0.0%) |
| 11 (14%) | 1 (5.6%) | 3 (16.7%) |
| 8 (10%) | 2 (11.1%) | 4 (22.2%) |
| 18 (23%) | 6 (33.3%) | 6 (33.3%) |
| 2 (3%) | 0 (0.0%) | 3 (16.7%) |
| 15 (19%) | 6 (33.3%) | 2 (11.1%) |
Resection | 62 (79%) | 15 (83.3%) | 15 (83.3%) |
Tumor location | |||
| 34 (43%) | 4 (22.2%) | 12 (66.6%) |
| 6 (8%) | 0 (0.0%) | 1 (5.6%) |
| 15 (19%) | 4 (22.2%) | 2 (11.1%) |
| 12 (15%) | 3 (16.7%) | 2 (11.1%) |
| 11 (14%) | 7 (38.9%) | 1 (5.6%) |
Internal test set | MODEL | DSC [index] | HD95 [mm] | Surface DSC [index] | APL [mm] | Volume GT [cm3] | Volume Segmentation [cm3] |
nnU-Net | 0.93 (0.86–0.95) | 0.9 (0.7–2.5) | 0.97 (0.90–0.98) | 876 (407–1298) | 26.0 (9.1–52.8) | 25.0 (92.7–56.9) | |
SynthSeg | 0.85 (0.67–0.91) | 2.2 (1.7–4.8) | 0.84 (0.70–0.89) | 2809 (2311–3622) | 26.0 (9.1–52.8) | 24.1 (8.8 –60.3) | |
Internal test set | nnU-Net | 0.84 (0.69–0.89) | 2.1 (1.6–5.8) | 0.75 (0.63–0.84) | 3653 (2612–5667) | 29.7 (15.3–56.9) | 22.9 (10.0–48.5) |
SynthSeg | 0.83 (0.63–0.89) | 2.9 (2.2–7.6) | 0.73 (0.55–0.78) | 4562 (3373–5280) | 29.7 (15.3–56.9) | 25.0 (9.8–56.0) |
Segmentations | Clinical Rating, Mean ± SD |
Internal dataset | |
| 3.5 ± 0.8 |
| 3.8 ± 0.5 |
| 2.6 ± 0.5 |
External dataset | |
| 2.9 ± 0.3 |
| 3.6 ± 0.6 |
| 2.6 ± 0.6 |
Internal test set | MODEL | DSC [-] | HD95 [mm] | Surface DSC [-] | Volume GT [cm3] | Volume Segmentation [cm3] |
nnU-Net | 0.87 (0.82–0.89) | 3.1 (1.8–4.6) | 0.85 (0.76–0.91) | 77.2 (57.2–96.7) | 86.8 (63.6–110.0) | |
SynthSeg | 0.80 (0.72–0.83) | 3.1 (2.4–13.3) | 0.73 (0.29–0.77) | 77.2 (57.2–96.7) | 85.5 (60.1 –106.7) | |
External test set | nnU-Net | 0.76 (0.69–0.79) | 2.3 (2.0–6.2) | 0.80 (0.69–0.83) | 78.3 (68.6–92.1) | 59.7 (52.3–75.1) |
SynthSeg | 0.74 (0.64–0.78) | 2.9 (2.1–7.7) | 0.77 (0.65–0.84) | 78.3 (68.6–92.1) | 59.1 (46.1–75.4) |
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Wubbels, M.; Ribeiro, M.; Wolterink, J.M.; van Elmpt, W.; Compter, I.; Hofstede, D.; Birimac, N.E.; Vaassen, F.; Palmgren, K.; Hansen, H.H.G.; et al. Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients. Cancers 2025, 17, 1598. https://doi.org/10.3390/cancers17101598
Wubbels M, Ribeiro M, Wolterink JM, van Elmpt W, Compter I, Hofstede D, Birimac NE, Vaassen F, Palmgren K, Hansen HHG, et al. Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients. Cancers. 2025; 17(10):1598. https://doi.org/10.3390/cancers17101598
Chicago/Turabian StyleWubbels, Mart, Marvin Ribeiro, Jelmer M. Wolterink, Wouter van Elmpt, Inge Compter, David Hofstede, Nikolina E. Birimac, Femke Vaassen, Kati Palmgren, Hendrik H. G. Hansen, and et al. 2025. "Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients" Cancers 17, no. 10: 1598. https://doi.org/10.3390/cancers17101598
APA StyleWubbels, M., Ribeiro, M., Wolterink, J. M., van Elmpt, W., Compter, I., Hofstede, D., Birimac, N. E., Vaassen, F., Palmgren, K., Hansen, H. H. G., Weide, H. L. v. d., Brouwer, C. L., Kramer, M. C. A., Eekers, D. B. P., & Zegers, C. M. L. (2025). Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients. Cancers, 17(10), 1598. https://doi.org/10.3390/cancers17101598