SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation

• URL: http://arxiv.org/abs/2411.11636v1
• Date: Mon, 18 Nov 2024 15:14:36 GMT
• Title: SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation
• Authors: Shiman Li, Jiayue Zhao, Shaolei Liu, Xiaokun Dai, Chenxi Zhang, Zhijian Song,
• Abstract summary: SuperPixel-Propagated Pseudo-label learning method is proposed to handle the inadequate supervisory information challenge in weakly semi-supervised segmentation. Our method achieves state-of-the-art performance on both tumor and organ segmentation datasets under the WSSS setting.
• Score: 10.127428696255848
• License:
• Abstract: Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation datasets. Weakly semi-supervised medical image segmentation is an essential application because it only requires a small amount of scribbles and a large number of unlabeled data to train the model, which greatly reduces the clinician's effort to fully annotate images. To handle the inadequate supervisory information challenge in weakly semi-supervised segmentation (WSSS), a SuperPixel-Propagated Pseudo-label (SP${}^3$) learning method is proposed, using the structural information contained in superpixel for supplemental information. Specifically, the annotation of scribbles is propagated to superpixels and thus obtains a dense annotation for supervised training. Since the quality of pseudo-labels is limited by the low-quality annotation, the beneficial superpixels selected by dynamic thresholding are used to refine pseudo-labels. Furthermore, aiming to alleviate the negative impact of noise in pseudo-label, superpixel-level uncertainty is incorporated to guide the pseudo-label supervision for stable learning. Our method achieves state-of-the-art performance on both tumor and organ segmentation datasets under the WSSS setting, using only 3\% of the annotation workload compared to fully supervised methods and attaining approximately 80\% Dice score. Additionally, our method outperforms eight weakly and semi-supervised methods under both weakly supervised and semi-supervised settings. Results of extensive experiments validate the effectiveness and annotation efficiency of our weakly semi-supervised segmentation, which can assist clinicians in achieving automated segmentation for organs or tumors quickly and ultimately benefit patients.

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