End-to-End Boundary Aware Networks for Medical Image Segmentation
This addresses the need for precise anatomical boundary segmentation in medical imaging, which is crucial for clinical diagnosis and treatment planning, but it appears incremental as it builds on existing CNN methods with added boundary-awareness.
The paper tackled the problem of medical image segmentation by proposing boundary aware CNNs that incorporate organ boundary information, resulting in more accurate segmentation results on the BraTS 2018 dataset for brain tumor segmentation.
Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, however, expert manual segmentation often relies on the boundaries of anatomical structures of interest. We propose boundary aware CNNs for medical image segmentation. Our networks are designed to account for organ boundary information, both by providing a special network edge branch and edge-aware loss terms, and they are trainable end-to-end. We validate their effectiveness on the task of brain tumor segmentation using the BraTS 2018 dataset. Our experiments reveal that our approach yields more accurate segmentation results, which makes it promising for more extensive application to medical image segmentation.