Ensemble Convolutional Neural Network Classification for Pancreatic Steatosis Assessment in Biopsy Images
Abstract
:1. Introduction
2. Materials and Methods
- An image segmentation stage employing image thresholding and morphological filtering techniques in 20 pancreatic biopsy images (with 20× magnification) to extract the tissue area from its background and filter circular white structures.
- Manual annotation of objects of interest in each 20× histological image and calculation of the semi-quantitative degree of steatosis by clinicians. At the same time, export of annotated objects in the form of image patches for applying transfer learning in four pretrained convolutional neural networks (CNNs).
- Classification of the segmented regions of interest in step 1 based on the majority of trained CNN models’ votes and eliminating most false-positive fat segmentation results.
- Calculation of the fat ratio for each 20× biopsy image and evaluation of the automated diagnostic method by determining its deviation from the semi-quantitative estimates of doctors.
2.1. Histological Image Dataset
2.2. Image Processing and Segmentation Stage
2.2.1. Tissue Region Extraction
2.2.2. Objects of Interest Segmentation
2.3. Histological Images Annotation
2.3.1. Semi-Quantitative Steatosis Evaluation
2.3.2. Exporting Training Data from Manual Annotations
2.4. Data Preprocessing and Deep Learning
2.4.1. Image Augmentation and Class Balancing
2.4.2. Transfer Learning in Pretrained CNN Models
2.4.3. Classification of Tissue Objects and Fat Ratio Calculation
Algorithm 1 Ensemble CNN System |
|
3. Results
3.1. Testing Performance Measurements
3.2. Visualization of Informative Features
3.3. Pancreatic Steatosis Quantification Results
3.4. Fat Regions Segmentation Similarity
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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Class Label | Initial Count | Removed Images | Image Augmentation | Augmented Count | Final Count |
---|---|---|---|---|---|
single | 2400 | 1080 | - | - | 1320 |
double | 342 | - | • horizontal flip (x-axis) • vertical flip (y-axis) • horizontal + vertical flip | 1026 | 1368 |
multiple | 335 | - | • horizontal flip (x-axis) • vertical flip (y-axis) • horizontal + vertical flip | 1005 | 1340 |
artifact | 870 | - | • horizontal + vertical flip | 870 | 1740 |
CNN Model | Trainable Parameters (Initial) | Frozen Weights | Trainable Parameters (Final) | Weight Learn Rate Factor | Bias Learn Rate Factor |
---|---|---|---|---|---|
AlexNet | 60,965,224 | - | 56,868,224 | 10 | 10 |
VGG-16 | 138,357,544 | • conv. block 1 • conv. block 2 | 134,260,544 | 20 | 20 |
VGG-19 | 143,667,240 | • conv. block 1 • conv. block 2 | 139,570,240 | 20 | 20 |
ResNet-50 | 25,583,592 | - | 23,534,592 | 10 | 10 |
CNN Model | Mean Performance Metrics (%) | |||||||
---|---|---|---|---|---|---|---|---|
Accuracy | Precision/PPV | Sensitivity/Recall | F1-Score | Specificity/TNR | NPV | ROC AUC | PRC AUC | |
AlexNet | 97.25 | 97.25 | 97.25 | 97.25 | 99.08 | 99.08 | 99.87 | 74.64 |
VGG-16 | 97 | 97.08 | 97 | 97.04 | 99 | 99.01 | 99.86 | 74.59 |
VGG-19 | 95.25 | 95.37 | 95.25 | 95.31 | 98.42 | 98.43 | 99.83 | 74.50 |
ResNet-50 | 94.25 | 94.37 | 94.25 | 94.31 | 98.08 | 98.11 | 99.58 | 73.80 |
Ensemble CNN | 98.25 | 98.25 | 98.25 | 98.25 | 99.42 | 99.42 | 99.73 | 99.47 |
Testing Image (20×) | Fat Ratio (%) Image Segmentation (FSegm) | Fat Ratio(%) Regions Classification (FClass) | Fat Ratio (%) Manual Annotations (FDoc) | |||||
---|---|---|---|---|---|---|---|---|
FACM | FAlexNet | FVGG-16 | FVGG-19 | FResNet-50 | FEnsembleCNN | FAnnot | ||
1 | 120216-Head | 1.83 | 1.72 | 1.63 | 1.70 | 1.59 | 1.66 | 1.74 |
2 | 120216-Tail | 1.58 | 1.37 | 1.21 | 1.32 | 1.30 | 1.29 | 1.31 |
3 | 120485-Tail | 1.89 | 1.71 | 1.59 | 1.65 | 1.65 | 1.66 | 1.77 |
4 | 120495-Body | 2.56 | 1.71 | 1.44 | 1.48 | 1.40 | 1.48 | 1.63 |
5 | 120495-Head | 1.88 | 1.64 | 1.55 | 1.57 | 1.57 | 1.62 | 1.79 |
6 | 121543-Body | 5.75 | 5.11 | 4.82 | 4.86 | 4.88 | 4.92 | 5.08 |
7 | 121543-Head | 6.54 | 6.12 | 5.76 | 5.89 | 5.89 | 6.00 | 5.99 |
8 | 122020-Body | 0.70 | 0.39 | 0.28 | 0.39 | 0.32 | 0.36 | 0.24 |
9 | 122020-Head | 1.15 | 0.59 | 0.39 | 0.53 | 0.43 | 0.53 | 0.41 |
10 | 122020-Tail | 1.22 | 0.61 | 0.45 | 0.50 | 0.48 | 0.47 | 0.44 |
11 | 122088-Body | 0.52 | 0.40 | 0.35 | 0.36 | 0.33 | 0.35 | 0.36 |
12 | 122088-Tail | 1.47 | 0.79 | 0.63 | 0.73 | 0.68 | 0.71 | 0.63 |
13 | 122288-Body | 3.22 | 1.82 | 0.97 | 1.07 | 1.26 | 1.07 | 0.99 |
14 | 122288-Tail | 0.68 | 0.41 | 0.34 | 0.42 | 0.36 | 0.39 | 0.35 |
15 | 122662-Body | 1.38 | 0.16 | 0.08 | 0.11 | 0.09 | 0.10 | 0.05 |
16 | 122662-Tail | 1.05 | 0.30 | 0.25 | 0.30 | 0.25 | 0.28 | 0.25 |
17 | 123538-Head | 3.04 | 2.52 | 2.40 | 2.50 | 2.50 | 2.47 | 2.55 |
18 | 123883-Tail | 2.84 | 2.62 | 2.41 | 2.52 | 2.33 | 2.56 | 2.39 |
19 | 123948-Tail | 1.84 | 1.57 | 1.42 | 1.51 | 1.41 | 1.50 | 1.44 |
20 | HP-0937 | 2.41 | 2.10 | 2.05 | 2.10 | 2.10 | 2.08 | 2.14 |
Mean Value: | 2.18 | 1.68 | 1.50 | 1.58 | 1.54 | 1.58 | 1.58 | |
StD: | 1.57 | 1.55 | 1.49 | 1.50 | 1.51 | 1.53 | 1.57 |
Testing Image (20×) | Classification Error (%) from Annotations (Cerr) | Image Segmentation Error (%) from Annotations (Serr) | |||||
---|---|---|---|---|---|---|---|
AlexNeterr | VGG-16err | VGG-19err | ResNet-50err | EnsembleCNNerr | ACMerr | ||
1 | 120216-Head | 0.02 | 0.11 | 0.04 | 0.15 | 0.08 | 0.09 |
2 | 120216-Tail | 0.06 | 0.10 | 0.01 | 0.02 | 0.02 | 0.26 |
3 | 120485-Tail | 0.06 | 0.18 | 0.12 | 0.12 | 0.11 | 0.13 |
4 | 120495-Body | 0.08 | 0.19 | 0.15 | 0.22 | 0.14 | 0.93 |
5 | 120495-Head | 0.16 | 0.24 | 0.23 | 0.22 | 0.18 | 0.09 |
6 | 121543-Body | 0.03 | 0.26 | 0.21 | 0.20 | 0.16 | 0.67 |
7 | 121543-Head | 0.13 | 0.23 | 0.10 | 0.10 | 0.00 | 0.54 |
8 | 122020-Body | 0.15 | 0.04 | 0.15 | 0.07 | 0.12 | 0.46 |
9 | 122020-Head | 0.18 | 0.02 | 0.12 | 0.02 | 0.12 | 0.74 |
10 | 122020-Tail | 0.17 | 0.01 | 0.07 | 0.04 | 0.03 | 0.78 |
11 | 122088-Body | 0.04 | 0.01 | 0.00 | 0.03 | 0.01 | 0.16 |
12 | 122088-Tail | 0.15 | 0.01 | 0.10 | 0.05 | 0.08 | 0.84 |
13 | 122288-Body | 0.83 | 0.02 | 0.08 | 0.27 | 0.08 | 2.22 |
14 | 122288-Tail | 0.06 | 0.01 | 0.06 | 0.01 | 0.04 | 0.33 |
15 | 122662-Body | 0.11 | 0.03 | 0.07 | 0.04 | 0.05 | 1.33 |
16 | 122662-Tail | 0.05 | 0.01 | 0.05 | 0.00 | 0.04 | 0.80 |
17 | 123538-Head | 0.03 | 0.15 | 0.05 | 0.05 | 0.09 | 0.49 |
18 | 123883-Tail | 0.23 | 0.02 | 0.13 | 0.06 | 0.17 | 0.45 |
19 | 123948-Tail | 0.13 | 0.02 | 0.07 | 0.04 | 0.05 | 0.39 |
20 | HP-0937 | 0.05 | 0.09 | 0.04 | 0.04 | 0.06 | 0.27 |
Mean Value: | 0.14 | 0.09 | 0.09 | 0.09 | 0.08 | 0.60 | |
StD: | 0.17 | 0.09 | 0.06 | 0.08 | 0.05 | 0.50 |
Testing Image (20×) | AlexNetDice | VGG-16Dice | VGG-19Dice | ResNet-50Dice | EnsembleCNNDice | |
---|---|---|---|---|---|---|
1 | 120216-Head | 91.8 | 90.3 | 91.6 | 91.7 | 91.0 |
2 | 120216-Tail | 88.0 | 91.0 | 89.1 | 90.0 | 90.3 |
3 | 120485-Tail | 87.7 | 86.4 | 86.5 | 86.2 | 87.0 |
4 | 120495-Body | 78.1 | 82.4 | 80.5 | 82.3 | 82.3 |
5 | 120495-Head | 87.0 | 87.5 | 86.4 | 87.0 | 88.5 |
6 | 121543-Body | 89.0 | 90.1 | 89.5 | 89.8 | 90.2 |
7 | 121543-Head | 90.5 | 91.7 | 91.2 | 90.7 | 90.9 |
8 | 122020-Body | 60.2 | 66.0 | 60.5 | 64.9 | 63.4 |
9 | 122020-Head | 70.0 | 76.9 | 73.1 | 66.4 | 73.3 |
10 | 122020-Tail | 71.6 | 80.7 | 77.5 | 77.4 | 79.9 |
11 | 122088-Body | 81.9 | 83.0 | 86.1 | 84.1 | 85.9 |
12 | 122088-Tail | 78.9 | 83.4 | 82.5 | 80.9 | 81.9 |
13 | 122288-Body | 63.1 | 84.2 | 80.5 | 77.4 | 83.6 |
14 | 122288-Tail | 79.9 | 86.9 | 79.7 | 79.0 | 81.3 |
15 | 122662-Body | 36.2 | 57.8 | 47.7 | 54.6 | 53.1 |
16 | 122662-Tail | 82.7 | 86.8 | 82.9 | 80.0 | 85.4 |
17 | 123538-Head | 90.6 | 92.5 | 91.6 | 89.0 | 91.6 |
18 | 123883-Tail | 87.7 | 89.8 | 89.5 | 83.6 | 88.2 |
19 | 123948-Tail | 84.4 | 88.4 | 87.0 | 85.6 | 87.0 |
20 | HP-0937 | 91.0 | 91.8 | 88.4 | 91.3 | 91.3 |
Mean Value: | 79.5 | 84.4 | 82.1 | 81.6 | 83.3 | |
StD: | 13.76 | 8.82 | 11.00 | 9.81 | 9.90 |
CNN Model | Time Complexity | Performance (%) | |||
---|---|---|---|---|---|
Training (min) | Testing (s) | Testing Accuracy | Fat Ratio Error (Mean) | Fat Ratio Error (StD) | |
AlexNet | 1.38 | 0.5 | 97.25 | 0.14 | 0.17 |
VGG-16 | 11.07 | 2.1 | 97 | 0.0879 | 0.09 |
VGG-19 | 13.56 | 2.3 | 95.25 | 0.0927 | 0.06 |
ResNet-50 | 9.58 | 1.9 | 94.25 | 0.088 | 0.08 |
Ensemble CNN | 35.59 | 15.1 | 98.25 | 0.08 | 0.05 |
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Arjmand, A.; Tsakai, O.; Christou, V.; Tzallas, A.T.; Tsipouras, M.G.; Forlano, R.; Manousou, P.; Goldin, R.D.; Gogos , C.; Glavas, E.; et al. Ensemble Convolutional Neural Network Classification for Pancreatic Steatosis Assessment in Biopsy Images. Information 2022, 13, 160. https://doi.org/10.3390/info13040160
Arjmand A, Tsakai O, Christou V, Tzallas AT, Tsipouras MG, Forlano R, Manousou P, Goldin RD, Gogos C, Glavas E, et al. Ensemble Convolutional Neural Network Classification for Pancreatic Steatosis Assessment in Biopsy Images. Information. 2022; 13(4):160. https://doi.org/10.3390/info13040160
Chicago/Turabian StyleArjmand, Alexandros, Odysseas Tsakai, Vasileios Christou, Alexandros T. Tzallas, Markos G. Tsipouras, Roberta Forlano, Pinelopi Manousou, Robert D. Goldin, Christos Gogos , Evripidis Glavas, and et al. 2022. "Ensemble Convolutional Neural Network Classification for Pancreatic Steatosis Assessment in Biopsy Images" Information 13, no. 4: 160. https://doi.org/10.3390/info13040160
APA StyleArjmand, A., Tsakai, O., Christou, V., Tzallas, A. T., Tsipouras, M. G., Forlano, R., Manousou, P., Goldin, R. D., Gogos , C., Glavas, E., & Giannakeas, N. (2022). Ensemble Convolutional Neural Network Classification for Pancreatic Steatosis Assessment in Biopsy Images. Information, 13(4), 160. https://doi.org/10.3390/info13040160