Disentangling chromosome overlaps by combining trainable shape models with classification evidence

Abstract

Resolving chromosome overlaps is an unsolved problem in automated chromosome analysis. We propose a method that combines evidence from classification and shape, based on trainable shape models. In evaluation using synthesized overlaps, certain cases are resolvable using shape evidence alone, but where this is misleading, classification evidence improves performance.