Article ; Online: Attentional adversarial training for few-shot medical image segmentation without annotations.
2024 Volume 19, Issue 5, Page(s) e0298227
Abstract: Medical image segmentation is a critical application that plays a significant role in clinical research. Despite the fact that many deep neural networks have achieved quite high accuracy in the field of medical image segmentation, there is still a ... ...
| Abstract | Medical image segmentation is a critical application that plays a significant role in clinical research. Despite the fact that many deep neural networks have achieved quite high accuracy in the field of medical image segmentation, there is still a scarcity of annotated labels, making it difficult to train a robust and generalized model. Few-shot learning has the potential to predict new classes that are unseen in training with a few annotations. In this study, a novel few-shot semantic segmentation framework named prototype-based generative adversarial network (PG-Net) is proposed for medical image segmentation without annotations. The proposed PG-Net consists of two subnetworks: the prototype-based segmentation network (P-Net) and the guided evaluation network (G-Net). On one hand, the P-Net as a generator focuses on extracting multi-scale features and local spatial information in order to produce refined predictions with discriminative context between foreground and background. On the other hand, the G-Net as a discriminator, which employs an attention mechanism, further distills the relation knowledge between support and query, and contributes to P-Net producing segmentation masks of query with more similar distributions as support. Hence, the PG-Net can enhance segmentation quality by an adversarial training strategy. Compared to the state-of-the-art (SOTA) few-shot segmentation methods, comparative experiments demonstrate that the proposed PG-Net provides noticeably more robust and prominent generalization ability on different medical image modality datasets, including an abdominal Computed Tomography (CT) dataset and an abdominal Magnetic Resonance Imaging (MRI) dataset. |
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| MeSH term(s) | Humans ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods ; Deep Learning ; Algorithms ; Magnetic Resonance Imaging/methods |
| Language | English |
| Publishing date | 2024-05-02 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2267670-3 |
| ISSN | 1932-6203 ; 1932-6203 |
| ISSN (online) | 1932-6203 |
| ISSN | 1932-6203 |
| DOI | 10.1371/journal.pone.0298227 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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