Chromosome Instance Segmentation

Chromosome instance segmentation aims to automatically identify and delineate individual chromosomes within microscopic images of metaphase spreads, a crucial step in karyotype analysis for diagnosing chromosomal disorders. Current research focuses on developing robust algorithms, often incorporating deep convolutional neural networks and image processing techniques like watershed algorithms, to overcome challenges posed by overlapping and clustered chromosomes. The development of large, accurately annotated datasets is also a key area of focus, enabling the training and evaluation of more effective segmentation models. Improved automation in this area promises faster and more accurate diagnosis of genetic diseases, reducing the need for extensive manual labor by trained cytogeneticists.