| Autor: |
Feng Gao, Minhao Hu, Min-Er Zhong, Shixiang Feng, Xuwei Tian, Xiaochun Meng, Ma-yi-di-li Ni-jia-ti, Zeping Huang, Minyi Lv, Tao Song, Xiaofan Zhang, Xiaoguang Zou, Xiaojian Wu |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Medical Image Analysis. 80:102515 |
| ISSN: |
1361-8415 |
| Popis: |
Since segmentation labeling is usually time-consuming and annotating medical images requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated segmentation dataset. We propose a novel weakly- and semi-supervised framework named SOUSA (Segmentation Only Uses Sparse Annotations), aiming at learning from a small set of sparse annotated data and a large amount of unlabeled data. The proposed framework contains a teacher model and a student model. The student model is weakly supervised by scribbles and a Geodesic distance map derived from scribbles. Meanwhile, a large amount of unlabeled data with various perturbations are fed to student and teacher models. The consistency of their output predictions is imposed by Mean Square Error (MSE) loss and a carefully designed Multi-angle Projection Reconstruction (MPR) loss. Extensive experiments are conducted to demonstrate the robustness and generalization ability of our proposed method. Results show that our method outperforms weakly- and semi-supervised state-of-the-art methods on multiple datasets. Furthermore, our method achieves a competitive performance with some fully supervised methods with dense annotation when the size of the dataset is limited. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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