Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation

• URL: http://arxiv.org/abs/2212.08774v1
• Date: Sat, 17 Dec 2022 01:07:21 GMT
• Title: Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation
• Authors: Qing En, Yuhong Guo
• Abstract summary: We propose a novel point-supervised contrastive variance method (PSCV) for medical image semantic segmentation. PSCV only requires one pixel-point from each organ category to be annotated. We show that the proposed method outperforms the state-of-the-art weakly supervised methods on point-supervised medical image semantic segmentation tasks.
• Score: 38.61378161105941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation methods typically rely on numerous dense annotated images for model training, which are notoriously expensive and time-consuming to collect. To alleviate this burden, weakly supervised techniques have been exploited to train segmentation models with less expensive annotations. In this paper, we propose a novel point-supervised contrastive variance method (PSCV) for medical image semantic segmentation, which only requires one pixel-point from each organ category to be annotated. The proposed method trains the base segmentation network by using a novel contrastive variance (CV) loss to exploit the unlabeled pixels and a partial cross-entropy loss on the labeled pixels. The CV loss function is designed to exploit the statistical spatial distribution properties of organs in medical images and their variance distribution map representations to enforce discriminative predictions over the unlabeled pixels. Experimental results on two standard medical image datasets demonstrate that the proposed method outperforms the state-of-the-art weakly supervised methods on point-supervised medical image semantic segmentation tasks.

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