Bounding boxes for weakly-supervised breast cancer segmentation in DCE-MRI

Abstract

Accurate segmentation of cancerous regions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is crucial for the diagnosis and prognosis assessment of high-risk breast cancer. Deep learning methods have achieved success in this task. However, their performance heavily relies on large-scale fully annotated training data, which are time-consuming and labor-intensive to acquire. To alleviate the annotation effort, we propose a simple yet effective bounding box supervised segmentation framework, which consists of a primary network and an ancillary network. To fully exploit the bounding box annotations, we initially train the ancillary network. Specifically, we integrate a bounding box encoder into the ancillary network to serve as a naive spatial attention mechanism, thereby enhancing feature distinction between voxels inside and outside the bounding box. Additionally, we convert uncertain voxel-wise labels inside bounding box into accurate projection labels, ensuring a noise-free initial training process. Subsequently, we adopt an alternating optimization scheme where self-training is performed to generate voxel-wise pseudo labels, and a regularized loss is optimized to correct potential prediction error. Finally, we employ knowledge distillation to guide the training of the primary network with the pseudo labels generated by the ancillary network. We evaluate our method on an in-house DCE-MRI dataset containing 461 patients with 561 biopsy-proven breast cancers (mass/non-mass: 319/242). Our method attains a mean Dice value of 81.42%, outcompeting other weakly-supervised methods in our experiments. Notably, for the non-mass-like lesions with irregular shapes, our method can still generate favorable segmentation with an average Dice of 79.31%. The code is publicly available at https://github.com/Abner228/weakly_box_breast_cancer_seg.