A segmentation-based approach for polyp counting in the wild

We address the problem of jellyfish polyp counting in underwater images. Modern methods utilize convolutional neural networks for feature extraction and work in two stages. First, hypothetical regions are proposed at potential locations, the features of the regions are extracted and classified according to the contained object. Such methods typically require a dense grid for region proposals, explicitly test various scales and are prone to failure in densely populated regions. We propose a segmentation-based polyp counter – SegCo. A convolutional neural network is trained to produce locally-circular segmentation masks on the polyps, which are then detected by localizing circularly symmetric areas in the segmented image. Detection stage is effcient and avoids a greedy search over position and scales. SegCo outperforms the current state-of-the-art object detector RetinaNet and the recent specialized polyp detection method PoCo by 2% and 24% in F-score, respectively, and sets a new state-of-the-art in polyp detection.