Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
This work addresses a domain-specific problem in medical imaging for clinical diagnosis, with incremental improvements in handling noise and view gaps.
The authors tackled multi-view echocardiographic sequence segmentation by proposing a recurrent aggregation learning method, achieving superior segmentation and classification accuracy with prominent temporal stability on datasets of 9000 and 1800 labeled images.
Multi-view echocardiographic sequences segmentation is crucial for clinical diagnosis. However, this task is challenging due to limited labeled data, huge noise, and large gaps across views. Here we propose a recurrent aggregation learning method to tackle this challenging task. By pyramid ConvBlocks, multi-level and multi-scale features are extracted efficiently. Hierarchical ConvLSTMs next fuse these features and capture spatial-temporal information in multi-level and multi-scale space. We further introduce a double-branch aggregation mechanism for segmentation and classification which are mutually promoted by deep aggregation of multi-level and multi-scale features. The segmentation branch provides information to guide the classification while the classification branch affords multi-view regularization to refine segmentations and further lessen gaps across views. Our method is built as an end-to-end framework for segmentation and classification. Adequate experiments on our multi-view dataset (9000 labeled images) and the CAMUS dataset (1800 labeled images) corroborate that our method achieves not only superior segmentation and classification accuracy but also prominent temporal stability.