Journal of Pathology Informatics

ORIGINAL ARTICLE
Year
: 2020  |  Volume : 11  |  Issue : 1  |  Page : 37-

Using image registration and machine learning to develop a workstation tool for rapid analysis of glomeruli in medical renal biopsies


David C Wilbur1, Jason R Pettus2, Maxwell L Smith3, Lynn D Cornell4, Alexander Andryushkin1, Richard Wingard1, Eric Wirch1 
1 Corista LLC, Concord, MA, USA
2 Department of Pathology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
3 Department of Pathology, Mayo Clinic, Scottsdale, AZ, USA
4 Department of Pathology, Mayo Clinic, Rochester, MN, USA

Correspondence Address:
Dr. David C Wilbur
Corista LLC, 9 Damonmill Square, Suite 6A, Concord, MA 01742
USA

Background: Prescreening of biopsies has the potential to improve pathologists' workflow. Tools that identify features and display results in a visually thoughtful manner can enhance efficiency, accuracy, and reproducibility. Machine learning for detection of glomeruli ensures comprehensive assessment and registration of four different stains allows for simultaneous navigation and viewing. Methods: Medical renal core biopsies (4 stains each) were digitized using a Leica SCN400 at ×40 and loaded into the Corista Quantum research platform. Glomeruli were manually annotated by pathologists. The tissue on the 4 stains was registered using a combination of keypoint- and intensity-based algorithms, and a 4-panel simultaneous viewing display was created. Using a training cohort, machine learning convolutional neural net (CNN) models were created to identify glomeruli in all stains, and merged into composite fields of views (FOVs). The sensitivity and specificity of glomerulus detection, and FOV area for each detection were calculated. Results: Forty-one biopsies were used for training (28) and same-batch evaluation (6). Seven additional biopsies from a temporally different batch were also evaluated. A variant of AlexNet CNN, used for object recognition, showed the best result for the detection of glomeruli with same-batch and different-batch evaluation: Same-batch sensitivity 92%, “modified” specificity 89%, average FOV size represented 0.8% of the total slide area; different-batch sensitivity 90%, “modified” specificity 98% and average FOV size 1.6% of the total slide area. Conclusions: Glomerulus detection in the best CNN model shows that machine learning algorithms may be accurate for this task. The added benefit of biopsy registration with simultaneous display and navigation allows reviewers to move from one machine-generated FOV to the next in all 4 stains. Together these features could increase both efficiency and accuracy in the review process.


How to cite this article:
Wilbur DC, Pettus JR, Smith ML, Cornell LD, Andryushkin A, Wingard R, Wirch E. Using image registration and machine learning to develop a workstation tool for rapid analysis of glomeruli in medical renal biopsies.J Pathol Inform 2020;11:37-37


How to cite this URL:
Wilbur DC, Pettus JR, Smith ML, Cornell LD, Andryushkin A, Wingard R, Wirch E. Using image registration and machine learning to develop a workstation tool for rapid analysis of glomeruli in medical renal biopsies. J Pathol Inform [serial online] 2020 [cited 2020 Nov 26 ];11:37-37
Available from: https://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=37;epage=37;aulast=Wilbur;type=0