Crowdsourcing for Chromosome Segmentation and Deep Classification

Monika Sharma, Oindrila Saha, Anand Sriraman, Ramya Hebbalaguppe, Lovekesh Vig, Shirish Karande; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 34-41

Observations of chromosomal segments or translocations during metaphase can indicate structural changes in cell genome, and is often used for diagnostic purposes. Karyotyping of the chromosomes under metaphase is done by characterizing the individual chromosomes in cell spread images. Currently considerable effort and time is spent to manually segment out overlapping chromosomes from cell images, and classifying the segmented chromosomes into one of the 24 types. There exists techniques which automate segmentation and classification of chromosomes with reasonable accuracy, but a human in the loop is often still required due to the criticality of domain. Therefore, we present a method to segment out and classify chromosomes for healthy patients using a combination of crowdsourcing for segmentation, pre-procesing and classification using deep learning. Experimental results are encouraging and promise to significantly reduce the cognitive burden of automatic karyotyping of chromosomes.