CCBox: Improving Box-supervised Nuclei Segmentation with Consistency Constraint

Nuclei segmentation is a critical step in the automated analysis of digitized microscopic images, which facilitates analysis of pathological images. The current state-of-the-art (SOTA) methods for nuclei segmentation require significant time and resources to provide pixel-level annotations for training. To reduce the labor-intensive annotation cost, we propose CCBox, a high-quality nuclei segmentation network that only requires bounding box annotations. We first generate hard pseudo labels for each nuclei within the bounding boxes using the traditional methods and then train a nuclei segmentation network with these hard pseudo labels. The major challenge lies in the presence of significant noise in the boundary regions of the nuclei due to the susceptibility of traditional methods to complex texture information in pathological images, impacting the model’s segmentation performance at the nucleus boundaries. To address this challenge, we propose the consistency constraint for similarity maps (CSM) strategy, which aggregates pixel features of the same semantics and enhances the discriminability of foreground and background features at the nucleus boundaries. Furthermore, to mitigate the overfitting of the model to noisy samples in the hard pseudo labels, we employ the hard-soft joint supervision strategy to supervise the student network. Extensive experiment results demonstrate that our CCBox significantly narrows the gap between box-supervised and fully-supervised nuclei segmentation methods.