Table 1 Summary of Relevant Computational Pathology Work in Musculoskeletal Research
Tissue Type | Author/Year | Aim/Objective | Species | Stain | Imaging Modality | ML/feature Extraction Type | Technique/Model | Transfer Learning | Biological Specimens | Images (N) | Magnification | Performance Reported |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Synovial Tissues | Kraan 2000 [39] | Quantification of CD3 and CD68+ cells | Human | IHC/DAB | Microscope, Camera | Knowledge Driven | Thresholding | No | 9 RA, 5 Control subjects | 70 (n=5/section) | 40× | DIA Significantly correlated with manual cell counts, Spearman ρ: 0.56–0.95 |
Haringman 2005 [59] | Quantification of CD68+ cells | Human | IHC/DAB | Microscope, Camera | Knowledge Driven | Thresholding | No | 88 subjects (n=176 samples) | NR | 40x | Validated in [39] | |
Rooney 2007 [60] | Quantification of CD3 and CD68+ cells | Human | IHC/DAB | Microscope, Camera | Knowledge Driven | Thresholding | No | 12 subjects (n≥6 samples/subject, n > 72) | 24 tissue sections (n=12 slides) | 1392×1040 pixel/image | ICC across sites: CD3+ 0.79; CD68+ 0.58; Spearman ρ Manual counts vs DIA : 0.62–0.98 | |
Morawietz 2008 [61] | Quantification of synovial features to validate synovitis score (enlargement of synovial lining (thickness), density of synovial stroma and inflammatory infiltrate (count)) | Human | H&E | Microscope, Camera | Knowledge Driven | Thresholding | No | 71 subjects (OA, n=22, PsA, n=7, RA, n=35, control, n=7) | NR | NR 584×720 pixel/image | Significant agreement in all measurements between the model and three independent pathology graders, Spearman ρ: 0.458–0.921 | |
Nuclear and cytoplasmic/ECM area | Mouse | H&E | Slide Scanner | Supervised, Knowledge Driven | Bayesian Classifier | No | NA | NA | 40× | Previously Validated [75] | ||
Venerito 2021 [43] | Quantification and classification of synovitis | Human | H&E | Microscope, Camera | Supervised, Data Driven | CNN/Resnet34 | Yes | 12 subjects | 150 | 4–20× | Validation Set - Acc.: 0.9; Prec.: 0.93; Rec.: 0.875 Test Set – Acc.: 1.0; Prec.: 1.0, Rec.: 1.0 | |
Cartilage | Knight 2001 [64] | Vimentin and microtubule spatial organization | Bovine | IHC-IF | Confocal | Knowledge driven | Convolutional Filters | No | NR | NR | 60× | Not validated |
Moussavi-Harami 2009 [65] | Automated and Objective implementation of the Mankin Scoring Scale | Human | Safranin-O | Microscope, Camera | Knowledge Driven | Custom Features Extraction | No | 18 subjects (femoral heads, n =12, femoral condyles, n=5, tibial plateau, n=7) | NR | 4× stitched (743,028 pixels/mm2 resolution) | Correlated well with Manikin Scoring (r 2=0.748) | |
Yang 2019 [44] | Chondrocyte detection, count, and boundary segmentation | Rabbit | Safranin-O | Microscope, Camera | Supervised, Data Driven | CNN/U-Net | No | NR | 260 | 256×256 pixel/image 0.32 μm/pixel | F1 scores: 0.86–0.90; segmentation accuracy: IoU=0.828; counted fewer chondrocytes than expert observer (p<0.001 paired t test) | |
Skeletal muscle | Klemencic 1998 [66] | Fiber Geometry | Human | Myofibrillar ATPase Activity | Microscope, Camera | Unsupervised, Knowledge Driven | Active Contour Model | NA | 1 subject | NR | 512×360 pixel/image 2.2 μm/pixel | Qualitative 92% correct by expert graders |
Kim 2007 [48] | Fiber geometry | Human | H&E | Microscope, Camera | Unsupervised, Knowledge Driven | Active Contour Model | NA | 5 subjects | 30 | 20× 640×480 pixel/image | 663/679 (98%) fibers correctly detected; | |
Sertel 2011 [67] | Fiber Geometry and Type | Rat | ATPase Activity | Microscope, Camera | Unsupervised, Knowledge Driven | Ridge detection | NA | 12 subjects | 25 | 10× 1280×1024 pixel/image | Overlap score: 91.3 ± 4.8% | |
Liu 2013 [68] | Fiber geometry, Type, Myonuclei Counting | Mouse | IHC-IF | Microscope, Camera | Unsupervised/Supervised, Knowledge Driven | Ridge detection, SVM | NA | NR | 20 | 20× | CSA Avg Diff: 0.88% Fiber type Avg Diff: 0.09% Nuclei counting Diff: 8.61% | |
Smith and Barton 2014 [69] | Fiber Geometry, Type, MHC, Capillary Density, and CNF | Mouse | IHC-IF | Microscope, Camera | Knowledge Driven | Filtering and Watershed | No | 8 subjects (n=4/group) | NR | NR | Difference Reported to Legacy Method (Simple Thresholding) CSA: 21.7% Fiber type: 7/177 fibers CNF: 9% | |
Wen 2018 [49] | Fiber Geometry, Type, and Myonuclei Counting | Mouse | IHC-IF | Microscope, Camera | Semi-supervised, Knowledge Driven | Watershed with Euclidean Distance K-Means Optimization | No | 16 (n=4/group) | NR | 20× | Accuracy of ≥94% for fiber number, fiber type distribution, fiber CSA, and myonuclear number | |
Miazaki 2015 [70] | Fiber Number and Geometry | Mouse | IHC-IF | Microscope, Camera—Stitched Into Mosaic | Unsupervised, Knowledge Driven | Filtering, Thresholding and Post-Hoc Shape Filtering | No | 6 subjects (n=3/group) | 6 20–30 stitched/sample | 800×600 pixels/image 0.7 μm/pixel | NR | |
Mayeuf-Louchart 2018 [71] | Fiber Number, Geometry, Type, CNF, Satellite Cells, and Vessel | Mouse | IHC-IF | Slide Scanner | Knowledge Driven | Filtering, Thresholding and Post-Hoc Shape Filtering | No | 9 subjects (n=5, injured, n=4, control) | NR | 20–40× 0.325–0.380 μm/pixel | No significant difference between expert graders and digital analysis in both uninjured and injured for all parameters, Mann-Whitney test p value: 0.4–0.7 | |
Reyes-Fernandez 2019 [72] | Fiber Number and Geometry | Human | IHC-IF | Microscope, Camera | Knowledge Driven | Filtering and Thresholding | No | 57 subjects | NR | 10× 9300×9900 pixels/image | Overall detection/segmentation of 89.3% of the total fibers (342/3212 not detected fibers across 10 samples analyzed); < 1% of the fibers misclassified (21/3212) | |
Kastenschmidt 2019 [45] | Fiber Number, Geometry, Type, and CNF | Human and Mouse | IHC-IF | Microscope, Camera—Stitched into Mosaic | Supervised, Knowledge Driven | Filtering and Thresholding; SVM | No | NR (Human) 108 subjects (Mouse) | NR (Human) NR (Mouse) | 10× (Human) 20× (Mouse) 1920×1440 pixels/image (Mouse) | Fiber number Acc.: 80–98%; CSA Acc.: 90–98%; CNF Acc.: 85–95%; Fiber Type Acc.: NR | |
Encarnacion-Rivera 2020 [73] | Fiber Number, Geometry and Type | Mouse | IHC-IF | Microscope, Camera—Stitched into Mosaic | Knowledge Driven | Convolutional Filtering; Random Forest; Thresholding | No | 32 subjects (n=29, C57BL/6J, n=3, mdx-4Cv) | ~192 6/subject | 10× | Count: r 2=0.99 with manual count CSA: Not Different than Manual annotation (2 annotators) Type: 1–5% False Positives | |
Other | Zhang 2016 [79] | Bone Fracture Healing Tissue Areas: New Cartilage, New Bone, New Fibrous Tissue, Bone Marrow and New Osteoblastic Area | Mouse | H&E – Orange G - Alcian Blue | Slide scanner | Knowledge Driven | Model Not Reported; Post-Hoc Area and Shape Adjustments | No | 5 subjects (Mouse) | 5 | 40× | ICCs between the Algorithm and Hand Drawn Areas: New Cartilage = 0.98, New Bone = 0.99, New Fibrous Tissue = 0.97 |
Xia 2021 [46] | Wound Healing via Area of Primary Granulation, Secondary Granulation and Chondrogenic Tissue over Time | Mouse | H&E | Slide scanner | Supervised, Knowledge Driven | Random Forest | No | 4 subjects (Mouse) | 4 | 40× | Good agreement between model and pathologist scores | |
Correia 2020 [47] | Develop DL-based score to mimic mRSS which discriminates SSc from normal skin | Human | Masson’s Trichrome | Slide Scanner | Unsupervised, Supervised, Data Driven | DCNN (Encoder of AlexNet); Principal Component Analysis; Logistic Regression | Yes | 92 subjects, 168 biopsies; Primary cohort (n = 6 subjects, 26 SSc biopsies); Secondary cohort (n = 60 SSc and 16 controls, 148 biopsies) | 100 randomly selected; Primary cohort (2600 image patches grouped by biopsy); Secondary cohort (7600 image patches grouped by biopsy) | 40× | Primary Cohort Biopsy Score Correlation with mRSS: R=0.55, p=0.01; Secondary Cohort Diagnostic Score Logistic Regression to Classify SSc from Healthy (0.5 cutoff): AUC = 0.99 Misclassification rate = 1.9% (training), 6.6% (test); Secondary Cohort Fibrosis Score significantly correlated with mRSS: R=0.70 (training), 0.55 (test) |