about
Image Segmentation to Distinguish Between Overlapping Human Chromosomes (arxiv.org)
2 points by jakek on Jan 13, 2018 | hide | past | pdf | discuss on HN

In plain words: A neural network that scans images in patches was trained to trace each chromosome's outline where two overlap in microscope pictures, so tangled chromosomes can be told apart automatically. Its outlines matched the true ones by 94.7% in the overlapping area, and similarly elsewhere.

Abstract

In medicine, visualizing chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosomes often overlap and it is necessary to identify and distinguish between the overlapping chromosomes. A segmentation solution that is fast and automated will enable scaling of cost effective medicine and biomedical research. We apply neural network-based image segmentation to the problem of distinguishing between partially overlapping DNA chromosomes. A convolutional neural network is customized for this problem. The results achieved intersection over union (IOU) scores of 94.7% for the overlapping region and 88-94% on the non-overlapping chromosome regions.

R. Lily Hu, Jeremy Karnowski, Ross Fadely, Jean-Patrick Pommier
arXiv:1712.07639 · cs.CV, cs.LG, q-bio.QM, stat.ML · submitted Dec 20, 2017
abstract · pdf · html · Presented at NIPS 2017 Machine Learning for Health

add comment on HN