Leveraging point annotations in segmentation learning with boundary loss
Eva Breznik Hoel Kervadec Filip Malmberg Joel Kullberg Håkan Ahlström Marleen de Bruijne Robin Strand
This paper investigates the combination of intensity-based distance maps with
boundary loss for point-supervised semantic segmentation. By design the
boundary loss imposes a stronger penalty on the false positives the farther
away from the object they occur. Hence it is intuitively inappropriate for weak
supervision, where the ground truth label may be much smaller than the actual
object and a certain amount of false positives (w.r.t. the weak ground truth)
is actually desirable. Using intensity-aware distances instead may alleviate
this drawback, allowing for a certain amount of false positives without a
significant increase to the training loss. The motivation for applying the
boundary loss directly under weak supervision lies in its great success for
fully supervised segmentation tasks, but also in not requiring extra priors or
outside information that is usually required -- in some form -- with existing
weakly supervised methods in the literature. This formulation also remains
potentially more attractive than existing CRF-based regularizers, due to its
simplicity and computational efficiency. We perform experiments on two
multi-class datasets; ACDC (heart segmentation) and POEM (whole-body abdominal
organ segmentation). Preliminary results are encouraging and show that this
supervision strategy has great potential. On ACDC it outperforms the CRF-loss
based approach, and on POEM data it performs on par with it. The code for all
our experiments is openly available.