  Aquaculture industries rely on the availability of accurate fish body
measurements, e.g., length, width and mass. Manual methods that rely on
physical tools like rulers are time and labour intensive. Leading automatic
approaches rely on fully-supervised segmentation models to acquire these
measurements but these require collecting per-pixel labels -- also time
consuming and laborious: i.e., it can take up to two minutes per fish to
generate accurate segmentation labels, almost always requiring at least some
manual intervention. We propose an automatic segmentation model efficiently
trained on images labeled with only point-level supervision, where each fish is
annotated with a single click. This labeling process requires significantly
less manual intervention, averaging roughly one second per fish. Our approach
uses a fully convolutional neural network with one branch that outputs
per-pixel scores and another that outputs an affinity matrix. We aggregate
these two outputs using a random walk to obtain the final, refined per-pixel
segmentation output. We train the entire model end-to-end with an LCFCN loss,
resulting in our A-LCFCN method. We validate our model on the DeepFish dataset,
which contains many fish habitats from the north-eastern Australian region. Our
experimental results confirm that A-LCFCN outperforms a fully-supervised
segmentation model at fixed annotation budget. Moreover, we show that A-LCFCN
achieves better segmentation results than LCFCN and a standard baseline. We
have released the code at \url{https://github.com/IssamLaradji/affinity_lcfcn}.
