Classification of microscopy images of Langerhans islets

Švihlík, Jan; Kybic, Jan; Hui‐Kang, David; Berková, Zuzana; Girman, Peter; Kříž, Jan; Zacharovová, K.

doi:10.1117/12.2043621

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Uneven illumination was corrected by fitting a second-order polynomial to approximately identified background pixels (29). The algorithm then uses a random forest classifier (3) to create a probability map of islets based on individual pixels, using RGB color components as features (30). A final binary classification (islets vs. nonislets) is derived from the probability map by employing spatial regularization using GraphCut (16).…”

Section: Methodsmentioning

confidence: 99%

Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

et al. 2016

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Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. Current computer-assisted methods require input from human operators. Here we describe two machine learning algorithms for islet quantification: the trainable islet algorithm (TIA) and the nontrainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method. The TIA is intended to automatically evaluate micrographs of isolated islets from future donors after being trained on micrographs from a limited number of past donors. Its training ability was first evaluated on 46 images from four donors. The pixel-to-pixel comparison, binary statistics, and islet DNA concentration indicated that the TIA was successfully trained, regardless of the color differences of the original images. Next, the TIA trained on the four donors was validated on an additional 36 images from nine independent donors. The TIA was fast (67 s/image), correlated very well with the FMS method (R2=1.00 and 0.92 for islet volume and islet count, respectively), and had small REs (0.06 and 0.07 for islet volume and islet count, respectively). Validation of the NPA against the EVA method using 70 images from 12 donors revealed that the NPA had a reasonable speed (69 s/image), had an acceptable RE (0.14), and correlated well with the EVA method (R2=0.88). Our results demonstrate that a fully automated analysis of clinical-grade micrographs of isolated pancreatic islets is feasible. The algorithms described herein will be freely available as a Fiji platform plugin.

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Section: Methodsmentioning

confidence: 99%

Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

et al. 2016

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“…[2][3][4] The islets contained in segmentation often touch and appear as a single object which gives incorrect results. The separation of touching objects can be performed by watershed transform applied in combination with distance transform computed at binary segmentation.…”

Section: Introductionmentioning

confidence: 97%

Automated separation of merged Langerhans islets

2016

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This paper deals with separation of merged Langerhans islets in segmentations in order to evaluate correct histogram of islet diameters. A distribution of islet diameters is useful for determining the feasibility of islet transplantation in diabetes. First, the merged islets at training segmentations are manually separated by medical experts. Based on the single islets, the merged islets are identified and the SVM classifier is trained on both classes (merged/single islets). The testing segmentations were over-segmented using watershed transform and the most probable back merging of islets were found using trained SVM classifier. Finally, the optimized segmentation is compared with ground truth segmentation (correctly separated islets).

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Classification of microscopy images of Langerhans islets

Cited by 2 publications

References 13 publications

Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

Automated separation of merged Langerhans islets

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