Jan Švihlík presents Automated Analysis of Microscopic Images of Isolated Pancreatic Islets
On 2016-10-27 10:00:00 at G205, Karlovo náměstí 13, Praha 2
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
non-trainable 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 (67sec/image), correlated very well
with the FMS method (R^2 = 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
sec/image), an acceptable RE (0.14), and correlated well with the EVA method
(R^2= 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.
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
non-trainable 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 (67sec/image), correlated very well
with the FMS method (R^2 = 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
sec/image), an acceptable RE (0.14), and correlated well with the EVA method
(R^2= 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.