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.

通过使用强度感知距离图和边界损失，本研究在点标注的语义分割中探讨了强度感知距离图与边界损失的组合。结果表明，这种监督策略具有巨大潜力，并在实验中取得了令人鼓舞的初步结果。

利用点注释在使用边界损失的分割学习中

Leveraging point annotations in segmentation learning with boundary loss

Widely used loss functions for CNN segmentation, e.g., Dice or cross-entropy,
are based on integrals over the segmentation regions. Unfortunately, for highly
unbalanced segmentations, such regional summations have values that differ by
several orders of magnitude across classes, which affects training performance
and stability. We propose a boundary loss, which takes the form of a distance
metric on the space of contours, not regions. This can mitigate the
difficulties of highly unbalanced problems because it uses integrals over the
interface between regions instead of unbalanced integrals over the regions.
Furthermore, a boundary loss complements regional information. Inspired by
graph-based optimization techniques for computing active-contour flows, we
express a non-symmetric $L_2$ distance on the space of contours as a regional
integral, which avoids completely local differential computations involving
contour points. This yields a boundary loss expressed with the regional softmax
probability outputs of the network, which can be easily combined with standard
regional losses and implemented with any existing deep network architecture for
N-D segmentation. We report comprehensive evaluations and comparisons on
different unbalanced problems, showing that our boundary loss can yield
significant increases in performances while improving training stability. Our
code is publicly available: this https URL .

基于轮廓空间的边界损失可弥补高度不平衡问题，结合标准区域损失，实现 N 维分割的深度网络架构，显著提高训练稳定性。