External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Image Acquisition and Manual Segmentation
2.3. Preprocessing and Postprocessing
2.4. Model Retraining
2.5. Model Testing
2.6. Code Availability
2.7. Statistical Analysis
3. Results
3.1. Demographics and Characteristics of the Study Cohort
3.2. Model Retraining and Testing
3.3. Analysis of Factors Influencing the Model Performance
3.4. Volume and Segmentation Agreement Analysis
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Training Cohort (n = 100) | Validation Cohort (n = 20) | Test Cohort (n = 920) | p-Value | |
---|---|---|---|---|---|
Age (years), mean ± SD | 70.5 ± 13.1 | 68 ± 13.4 | 69.6 ± 14.2 | 0.84 1 | |
Sex, n (%) | |||||
Male | 41 (41) | 11 (55) | 516 (56) | ||
Female | 59 (59) | 9 (45) | 404 (44) | ||
NIHSS score, median (IQR) | 7.5 (10) | 10 (10) | 7 (5) | 0.70 1 | |
GCS score, median (IQR) | 13 (7) | 14 (4) | 13 (8) | 0.11 2 | |
Symptom onset to imaging (hours), median (IQR) | 4.3 (13.6) | 3.9 (7.6) | 4.23 (12.8) | 0.89 2 | |
ICH location, n (%) | |||||
Lobar | 44 (44) | 6 (30) | 338 (36.7) | ||
Deep | 46 (46) | 7 (35) | 455 (49.5) | ||
Brainstem | 3 (3) | 3 (15) | 41 (4.5) | ||
Cerebellum | 7 (7) | 4 (20) | 86 (9.3) | ||
ICH + IVH volume (ml), mean ± SD | 83.8 ± 47.2 | 56.4 ± 89.7 | 76.5 ± 44.9 | 0.67 2 | |
ICH volume (ml), mean ± SD | 27.7 ± 30.2 | 34.2 ± 30.9 | 44.1 ± 14.2 | 0.30 2 | |
IVH volume (ml), mean ± SD | 61.1 ± 49.1 | 22.2 ± 77.1 | 34.5 ± 42.5 | 0.64 2 |
Metric | All Locations | Deep | Lobar | Brainstem | Cerebellum |
---|---|---|---|---|---|
OM | |||||
DSC | 0.84 (0.73, 0.88) | 0.86 (0.80, 0.89) | 0.84 (0.78, 0.89) | 0.71 (0.46, 0.78) | 0.48 (0.23, 0.64) |
Sensitivity | 0.79 (0.65, 0.86) | 0.85 (0.79, 0.91) | 0.80, (0.70, 0.87) | 0.58 (0.38, 0.74) | 0.34 (0.13, 0.49) |
PPV | 0.93 (0.85, 0.97) | 0.91 (0.85, 0.95) | 0.99 (0.85, 0.97) | 0.88 (0.76, 0.94) | 0.94 (0.76, 0.99) |
RM | |||||
DSC | 0.83 (0.74, 0.88) | 0.87 (0.81, 0.90) | 0.83 (0.72, 0.88) | 0.77 (0.57, 0.83) | 0.79 (0.65, 0.84) |
Sensitivity | 0.80 (0.69, 0.87) | 0.85 (0.79, 0.91) | 0.79 (0.63, 0.88) | 0.72 (0.57, 0.79) | 0.75 (0.59, 0.84) |
PPV | 0.91 (0.84, 0.95) | 0.91 (0.85, 0.95) | 0.92 (0.63, 0.88) | 0.87 (0.77, 0.94) | 0.88 (0.79, 0.94) |
t1 OM vs. RM (padj-value) | |||||
DSC | −5.9 (0.001) | 1.64 (ns) | 4.57 (0.001) | 1.90 (ns) | 12.94 (0.001) |
Sensitivity | 1.45 (ns) | 3.05 (0.036) | 4.03 (0.001) | 3.33 (0.03) | 16.49 (0.001) |
PPV | −7.23 (0.001) | 0.12 (ns) | 2.33 (ns) | 0.02 (ns) | 0.30 (ns) |
OM | RM | |||||
---|---|---|---|---|---|---|
Parameter | Slope | SD | p-Value | Slope | SD | p-Value |
0.75 | 0.01 | <0.001 | 0.78 | 0.01 | <0.001 | |
Location (in respect to deep location) | ||||||
Lobar | −0.04 | 0.01 | <0.01 | −0.06 | 0.01 | <0.001 |
Brainstem | −0.20 | 0.03 | <0.001 | −0.18 | 0.03 | <0.001 |
Cerebellum | −0.32 | 0.02 | <0.001 | −0.08 | 0.02 | <0.001 |
Volume (mm3) | 0.00 | 0.00 | <0.001 | 0.00 | 0.00 | <0.001 |
IVH Presence | 0.02 | 0.01 | 0.17 | 0.02 | 0.01 | 0.15 |
Center (in respect to Berlin, DE) | ||||||
Hamburg, DE | 0.003 | 0.01 | 0.81 | −0.02 | 0.013 | 0.09 |
Pavia, IT | 0.008 | 0.02 | 0.73 | −0.01 | 0.023 | 0.66 |
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Cao, H.; Morotti, A.; Mazzacane, F.; Desser, D.; Schlunk, F.; Güttler, C.; Kniep, H.; Penzkofer, T.; Fiehler, J.; Hanning, U.; et al. External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage. J. Clin. Med. 2023, 12, 4005. https://doi.org/10.3390/jcm12124005
Cao H, Morotti A, Mazzacane F, Desser D, Schlunk F, Güttler C, Kniep H, Penzkofer T, Fiehler J, Hanning U, et al. External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage. Journal of Clinical Medicine. 2023; 12(12):4005. https://doi.org/10.3390/jcm12124005
Chicago/Turabian StyleCao, Haoyin, Andrea Morotti, Federico Mazzacane, Dmitriy Desser, Frieder Schlunk, Christopher Güttler, Helge Kniep, Tobias Penzkofer, Jens Fiehler, Uta Hanning, and et al. 2023. "External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage" Journal of Clinical Medicine 12, no. 12: 4005. https://doi.org/10.3390/jcm12124005