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We propose a novel Attentional Scale Sequence Fusion based You Only Look Once(YOLO) framework (ASF-YOLO) which combines spatial and scale features foraccurate and fast cell instance segmentation. Built on the YOLO segmentationframework, we employ the Scale Sequence Feature Fusion (SSFF) module to enhancethe multi-scale information extraction capability of the network, and theTriple Feature Encoder (TPE) module to fuse feature maps of different scales toincrease detailed information. We further introduce a Channel and PositionAttention Mechanism (CPAM) to integrate both the SSFF and TPE modules, whichfocus on informative channels and spatial position-related small objects forimproved detection and segmentation performance. Experimental validations ontwo cell datasets show remarkable segmentation accuracy and speed of theproposed ASF-YOLO model. It achieves a box mAP of 0.91, mask mAP of 0.887, andan inference speed of 47.3 FPS on the 2018 Data Science Bowl dataset,outperforming the state-of-the-art methods. The source code is available athttps://github.com/mkang315/ASF-YOLO.

ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation