Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions

Abstract

Medical image annotation is scarce and costly. Few-shot segmentation has been widely used in medical image from only a few annotated examples. However, its research on lesion segmentation for lung diseases is still limited, especially for pulmonary aspergillosis. Lesion areas usually have complex shapes and blurred edges. Lesion segmentation requires more attention to deal with the diversity and uncertainty of lesions. To address this challenge, we propose MSPO-Net, a multilevel support-assisted prototype optimization network designed for few-shot lesion segmentation in computerized tomography (CT) images of lung diseases. MSPO-Net learns lesion prototypes from low-level to high-level features. Self-attention threshold learning strategy can focus on the global information and obtain an optimal threshold for CT images. Our model refines prototypes through a support-assisted prototype optimization module, adaptively enhancing their representativeness for the diversity of lesions and adapting to the unseen lesions better. In clinical examinations, CT is more practical than X-rays. To ensure the quality of our work, we have established a small-scale CT image dataset for three lung diseases and annotated by experienced doctors. Experiments demonstrate that MSPO-Net can improve segmentation performance and robustness of lung disease lesion. MSPO-Net achieves state-of-the-art performance in both single and unseen lung disease segmentation, indicating its potentiality to reduce doctors' workload and improve diagnostic accuracy. This research has certain clinical significance. Code is available at https://github.com/Tian-Yuan-ty/MSPO-Net .

Keywords: Chest CT image; Few-shot lesion segmentation; Lung diseases; Self-attention threshold learning strategy; Support-assisted prototype optimization.

Fig. 1

Chest CT images of three lung diseases and their annotation ground truth, (a) secondary pulmonary tuberculosis, (b) pulmonary aspergillosis, (c) lung adenocarcinoma.

Fig. 2

Illustration of the MSPO-Net during training stage. GT denotes the ground truth segmentation masks. Loss is computed between the predicted segmentation result and the GT mask of query. See more details in Sect. 3.3 and 3.4.

Fig. 3

Illustration of the MSPO-Net during testing stage. When updating the prototypes, the optimization process is guided by the gradient of loss. See more details in Sect. 3.5.

Fig. 4

Process of multilevel prototypes learning.

Fig. 5

The proposed self-attention threshold learning strategy.

Fig. 6

The simplified schematics of support-assisted prototype optimization.

Fig. 7

Qualitative results of our proposed MSPO-Net in lesion segmentation for secondary pulmonary tuberculosis (row 1), pulmonary aspergillosis (row 2) and lung adenocarcinoma (row 3). The deep-red part is the ground truth, and the light-red part is the predicted mask.

Fig. 8

Qualitative comparison of predicted masks and ground truth on lesion edges. The deep-red part is the ground truth, and the light-red part is the predicted mask. (a) Good performance, (b) ordinary performance.

Fig. 9

Generalization qualitative results of our proposed MSPO-Net, including successful cases (row 1 and 2) and failed cases (row 3). The deep-red part is the ground truth, and the light-red part is the predicted mask.

Fig. 10

MIoU and BIoU obtained at different iteration times of the support-assisted prototype optimization module.