Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patients
2.2. MR Imaging
2.3. Labeling Training Data
2.4. Data Preparation
2.5. Stratification
2.6. Deep Learning Model Training
2.7. Resolving Conflicts between Different Organs
2.8. Image Cropping
2.9. External and Prospective Validation
2.10. Time Savings and Reproducibility with Model Assisted Contouring
2.11. Statistical Analysis
3. Results
3.1. Model Accuracy
3.2. Time Savings with Model-Assisted Annotation
3.3. Interobserver Variability Improvement with Model Assisted Annotations
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Table of Abbreviations
ADPKD | Autosomal Dominant Polycystic Kidney Disease |
CKD | Chronic Kidney Disease |
CKD-EPI | Chronic Kidney Disease Epidemiology Collaboration |
CNN | Convolutional Neural Network |
CT | Computerized Tomography |
DICOM | Digital Imaging and Communications in Medicine |
DSC | Dice Similarity Coefficient |
eGFR | Estimated Glomerular Filtration Rate (based on CKD-EPI method) |
ESKD | End Stage Kidney Disease |
HASTE | Half Fourier Single-shot Turbo Spin-Echo |
MRI | Magnetic Resonance Imaging |
NIfTI | Neuroimaging Informatics Technology Initiative |
PACS | Picture Archiving and Communication System |
SSFP | Steady State Free Precession |
SSFSE | Single-Shot Fast Spin Echo |
TE | Time to Echo |
TKV | Total Kidney Volume |
ht-TKV | Height Adjusted Total Kidney Volume |
TR | Time to Repeat |
References
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First Author | Modality | Year | ADPKD subjects | Segmentation Methodology | Dice Score | Other Metrics | Organ |
---|---|---|---|---|---|---|---|
Sharma [26] | CT | 2017 | 125 | 2D VGG-16 FCN | 0.86 | 7.8% | Kidney |
Keshwani [24] | CT | 2018 | 203 CT scans ** | Multi-task 3D FCN | 0.95 | 3.86% | Kidney |
Shin [27] | CT | 2020 | 214 | 3D V-net | 0.961 * | 95% within 3% | Kidney + Liver |
Onthoni [25] | CT | 2020 | 97 | 2D SSD Inception Network V2 | - | Images: mAP: 94% Subjects: mAP: 82% | Kidney |
Hsiao [23] | CT | 2022 | 210 | FPN + EfficientNet | 0.969 | - | Kidney |
Jagtap [28] | US | 2022 | 22 | 2D U-Net | 0.80 | 4.12% | Kidney |
Kim [29] | MRI Cor T2 fatsat | 2016 | 60 | SPPM + PSC | 0.88 | MCC: 0.97 | Kidney |
Kline [30] | MRI Cor T2 | 2017 | 2000 scans ** | 2D U-Net + ResNet-like encoder | 0.97 | 0.68% | Kidney |
Guangrui [31] | MRI Axial + Cor T1 | 2019 | 305 | 3D VB-Net *** | RK-0.958 LK-0.965 | - | Kidney |
Van Gastel [32] | MRI Cor T2 fatsat | 2019 | 145 | 2D U-Net | - | LK: 0.96 RK: 0.95 TKV: 0.96 Liver: 0.95 | Kidney + Liver |
Kline [33] | MRI Cor T2 +/−fatsat | 2020 | 60 | 2D U-Net + ResNet-like encoder | 1st Reader: 0.86 2nd Reader: 0.84 | 1st Reader: 3.9% 2nd Reader: 8% | Kidney cysts |
Goel [22] | MRI Axial T2 | 2022 | 173 | 2D U-Net + EfficientNet encoder | External: 0.98 Prospective: 0.97 | External: 2.6% Prospective: 3.6% | Kidney |
Raj [34] | MRI Cor T1 | 2022 | 100 | 2D Attention U-Net | 0.922 | MSSD: 0.922 and 1.09 mm | Kidney |
Taylor [35] | MRI | 2022 | 227 Scans | 3D U-Net | 0.96 each kidney | LK:1.8% RK:1.79% | Kidney |
Parameter | Training/Validation Data | External Test Set | Prospective Test Set |
---|---|---|---|
Number of Patients | 215 | 30 | 30 |
Number of MR exams | 260 | 30 | 30 |
DICOM images | 9540 | 1368 | 2137 |
Male:Female (%male) | 98:117 (46%) | 17:13 (57%) | 11:19 (37%) |
Age at scan (years) | 49 ± 14 | 49 ± 16 | 46 ± 15 |
eGFR (mL/min/1.73 m2) | 68 ± 28 | 85 ± 30 | 72 ± 34 |
Total Kidney Volume (mL) * | 1287 (669–2213) | 1334 (693–2376) | 1444 (885–2020) |
Ht-TKV (mL/m) * | 757 (415–1275) | 777 (393–1297) | 837 (550–1234) |
Mayo class ** -Report N and % | |||
A | 29 (13%) | 4 (13%) | 1 (3%) |
B | 58 (27%) | 8 (27%) | 6 (20%) |
C | 70 (33%) | 7 (24%) | 13 (44%) |
D | 34 (16%) | 10 (33%) | 7 (23%) |
E | 24 (11%) | 1 (3%) | 3 (10%) |
Race-Report N and % | |||
Asian | 10 (5%) | 1 (3%) | 4 (13%) |
White | 148 (69%) | 23 (77%) | 16 (53%) |
Black | 14 (6%) | 1 (3%) | 2 (67%) |
Unknown | 43 (20%) | 5 (17%) | 8 (27%) |
(A) | ||||
---|---|---|---|---|
External Test Set | Right Kidney | Left Kidney | Liver | Spleen |
Model volume (mL) Corrected model volume (mL) | 617 (327–1009) 608 (316–1041) | 582 (416–1289) 582 (365–1285) | 1706 (1292–2087) 1684 (1287–2076) | 220 (145–274) 222 (157–280) |
DSC | 0.96 | 0.98 | 0.97 | 0.96 |
Concordance Coefficient | >0.99 | >0.99 | 0.98 | 0.99 |
RMS error (mL) | 42 | 39 | 258 | 17 |
Average % error | 7% | 3% | 3% | 1% |
Number with zero error | 5 (17%) | 6 (20%) | 1 (3%) | 7 (23%) |
(B) | ||||
Prospective Test Set | Right Kidney | Left Kidney | Liver | Spleen |
Model volume (mL) Corrected model volume (mL) | 625 (370–1000) 650 (394–998) | 729 (481–1039) 768 (485–1043) | 1711 (1489–2065) 1727 (1495–2051) | 244 (177–315) 241 (175–318) |
DSC | 0.96 | 0.96 | 0.96 | 0.95 |
Concordance Coefficient | >0.99 | >0.99 | >0.99 | 0.98 |
RMS error (mL) | 112 | 65 | 112 | 37 |
Average % error | 6% | 5% | 5% | 1% |
# with zero error | 2 (7%) | 3 (20%) | 2 (7%) | 2 (7%) |
Manual Segmentation | Model Assisted Segmentation | Time Savings | p-Value | |
---|---|---|---|---|
Right Kidney | 7:39 ± 2:26 | 4:31 ± 1:34 | 3:08 (41%) | 0.004 |
Left Kidney | 7:34 ± 3:44 | 4:16 ± 1:35 | 3:19 (44%) | 0.01 |
Liver | 12:49 ± 6:10 | 8:49 ± 3:52 | 3:59 (31%) | 0.007 |
Spleen | 4:13 ± 0:48 | 2:04 ± 0:59 | 2:09 (51%) | 0.0003 |
Total | 33:04 ± 8:05 | 19:17 ± 7:19 | 13:47 (42%) | 0.001 |
Volume Measurement Standard Deviations | |||
---|---|---|---|
Manual Segmentation (mL) | Model Assisted Segmentation (mL) | p-Value | |
Right Kidney Left Kidney | 14 10 | 7 5 | 0.02 0.07 |
Liver | 55 | 11 | 0.001 |
Spleen | 14 | 5 | 0.001 |
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Sharbatdaran, A.; Romano, D.; Teichman, K.; Dev, H.; Raza, S.I.; Goel, A.; Moghadam, M.C.; Blumenfeld, J.D.; Chevalier, J.M.; Shimonov, D.; et al. Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease. Tomography 2022, 8, 1804-1819. https://doi.org/10.3390/tomography8040152
Sharbatdaran A, Romano D, Teichman K, Dev H, Raza SI, Goel A, Moghadam MC, Blumenfeld JD, Chevalier JM, Shimonov D, et al. Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease. Tomography. 2022; 8(4):1804-1819. https://doi.org/10.3390/tomography8040152
Chicago/Turabian StyleSharbatdaran, Arman, Dominick Romano, Kurt Teichman, Hreedi Dev, Syed I. Raza, Akshay Goel, Mina C. Moghadam, Jon D. Blumenfeld, James M. Chevalier, Daniil Shimonov, and et al. 2022. "Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease" Tomography 8, no. 4: 1804-1819. https://doi.org/10.3390/tomography8040152