Abstract
We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection problem, rather than a pixel-level classification as in previous works. This outlook on segmentation may allow for boundary delineation under challenging scenarios such as where noise artefacts may be present within the region-of-interest (ROI) boundaries, where traditional pixel-level classification-based weakly supervised methods may not be able to effectively segment the ROI. Particularly of interest, ultrasound images, where intensity values represent acoustic impedance differences between boundaries, may also benefit from the boundary delineation approach. Our method uses reinforcement learning to train a controller function to localise boundaries of ROIs using a reward derived from a pre-trained boundary-presence classifier. The classifier indicates when an object boundary is encountered within a patch, as the controller modifies the patch location in a sequential Markov decision process. The classifier itself is trained using only binary patch-level labels of object presence, which are the only labels used during training of the entire boundary delineation framework, and serves as a weak signal to inform the boundary delineation. The use of a controller function ensures that a sliding window over the entire image is not necessary. It also prevents possible false-positive or -negative cases by minimising number of patches passed to the boundary-presence classifier. We evaluate our proposed approach for a clinically relevant task of prostate gland segmentation on trans-rectal ultrasound images. We show improved performance compared to other tested weakly supervised methods, using the same labels e.g., multiple instance learning.
Abstract (translated)
我们提出了boundary-RL,一种 novel 弱监督分割方法,仅使用块级标签进行训练。我们设想分割是一种边界检测问题,而不是在以前的作品中所采用的像素级分类问题。这种对分割的看法可以在遇到挑战的情况下实现边界分割,例如在兴趣区域(ROI)的边界内可能存在噪声效应,传统的基于像素级分类的弱监督方法可能无法有效地分割 ROI。尤其是,超声波图像,其强度值表示边界之间的 acoustic impedance 差异,也可能受益于边界分割方法。我们的方法使用强化学习来训练控制器函数,通过从预先训练的边界存在分类器中获取奖励,来定位 ROI 的边界。分类器表示何时对象边界在一个块内出现,因为控制器在Sequential 马尔可夫决策过程中对块位置进行修改。分类器本身仅使用二进制块级标签表示物体存在,这些标签是在整个边界分割框架训练中使用的唯一的标签,并成为告诉边界分割的弱信号。使用控制器函数确保了整个图像不必进行滑动窗口。它还可以防止可能的假阳性或阴性情况,通过最小化传递给边界存在分类器的块的数量。我们评估了我们所提出的方法在跨Rectified房地产分割相关任务中的表现。我们表现出与其他测试的弱监督方法相比更好的性能,使用相同的标签,例如多实例学习。
URL
https://arxiv.org/abs/2308.11376
