  Traditional sperm morphology analysis is based on tedious manual annotation.
Automated morphology analysis of a high number of sperm requires accurate
segmentation of each sperm part and quantitative morphology evaluation.
State-of-the-art instance-aware part segmentation networks follow a
"detect-then-segment" paradigm. However, due to sperm's slim shape, their
segmentation suffers from large context loss and feature distortion due to
bounding box cropping and resizing during ROI Align. Moreover, morphology
measurement of sperm tail is demanding because of the long and curved shape and
its uneven width. This paper presents automated techniques to measure sperm
morphology parameters automatically and quantitatively. A novel attention-based
instance-aware part segmentation network is designed to reconstruct lost
contexts outside bounding boxes and to fix distorted features, by refining
preliminary segmented masks through merging features extracted by feature
pyramid network. An automated centerline-based tail morphology measurement
method is also proposed, in which an outlier filtering method and endpoint
detection algorithm are designed to accurately reconstruct tail endpoints.
Experimental results demonstrate that the proposed network outperformed the
state-of-the-art top-down RP-R-CNN by 9.2% [AP]_vol^p, and the proposed
automated tail morphology measurement method achieved high measurement
accuracies of 95.34%,96.39%,91.2% for length, width and curvature,
respectively.
