FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation
This work addresses a critical problem in medical imaging for diagnosing and monitoring brain metastases patients, but it is incremental as it builds on existing CNN-based methods with specific enhancements.
The paper tackles the challenge of accurately segmenting small brain metastases in MR images, which is difficult due to feature loss in CNNs and irregular tumor shapes, and proposes FANCL, a model that uses feature-guided attention and curriculum learning to improve segmentation performance, achieving significant gains on the BraTS-METS 2023 dataset.
Accurate segmentation of brain metastases (BMs) in MR image is crucial for the diagnosis and follow-up of patients. Methods based on deep convolutional neural networks (CNNs) have achieved high segmentation performance. However, due to the loss of critical feature information caused by convolutional and pooling operations, CNNs still face great challenges in small BMs segmentation. Besides, BMs are irregular and easily confused with healthy tissues, which makes it difficult for the model to effectively learn tumor structure during training. To address these issues, this paper proposes a novel model called feature-guided attention network with curriculum learning (FANCL). Based on CNNs, FANCL utilizes the input image and its feature to establish the intrinsic connections between metastases of different sizes, which can effectively compensate for the loss of high-level feature from small tumors with the information of large tumors. Furthermore, FANCL applies the voxel-level curriculum learning strategy to help the model gradually learn the structure and details of BMs. And baseline models of varying depths are employed as curriculum-mining networks for organizing the curriculum progression. The evaluation results on the BraTS-METS 2023 dataset indicate that FANCL significantly improves the segmentation performance, confirming the effectiveness of our method.