Research on Improving Chromosome Segmentation in Hybrid Task Cascade
Aiming at the problems of time-consuming and poor accuracy in manually segmenting instances in chromosome images,a chromosome instance segmentation method based on improved Hybrid Task Cascade model was proposed.Firstly,a chromosome enhancement strategy based on case operation was proposed to expand the chromo-some data set with small amount and insufficient information.Then PAFPN was used to replace the feature pyramid module in Hybrid Task Cascade to retain the shallow feature information and improve the accuracy of locating and segmenting chromosome instances.For overlapping chromosome clusters,Soft-NMS method was introduced to improve the screening of candidate frames and retain more chromosome bounding boxes.Finally,the results of the test set were compared with other models.The average accuracy(mAP),AP50 and AP75were used to evaluate the performance of the model.Through the evaluation and verification of the self-collected mitotic metaphase chromosome microscopic images,the average accuracy of bounding box positioning and segmentation are 80.71%and 76.89%respectively.Experiments show that this method has good segmentation effect on chromosome image dataset.
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