Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound
This work addresses a domain-specific problem for medical imaging researchers and clinicians by providing a novel segmentation method for brain ultrasound, though it is incremental as it builds on existing neural network techniques.
The paper tackled the challenging problem of segmenting grey and white matter in 3D cranial ultrasound, which had not been addressed before, by training a multi-scale fully convolutional neural network with weak annotations and transfer learning, achieving Dice scores of 0.7080 for grey matter and 0.8402 for white matter.
Although the segmentation of brain structures in ultrasound helps initialize image based registration, assist brain shift compensation, and provides interventional decision support, the task of segmenting grey and white matter in cranial ultrasound is very challenging and has not been addressed yet. We train a multi-scale fully convolutional neural network simultaneously for two classes in order to segment real clinical 3D ultrasound data. Parallel pathways working at different levels of resolution account for high frequency speckle noise and global 3D image features. To ensure reproducibility, the publicly available RESECT dataset is utilized for training and cross-validation. Due to the absence of a ground truth, we train with weakly annotated label. We implement label transfer from MRI to US, which is prone to a residual but inevitable registration error. To further improve results, we perform transfer learning using synthetic US data. The resulting method leads to excellent Dice scores of 0.7080, 0.8402 and 0.9315 for grey matter, white matter and background. Our proposed methodology sets an unparalleled standard for white and grey matter segmentation in 3D intracranial ultrasound.