Unified Multi-scale Feature Abstraction for Medical Image Segmentation
This work addresses the need for more accurate segmentation in medical images, such as for liver cancer diagnosis, but appears incremental as it builds on existing encoder-decoder architectures like FCN and U-Net.
The paper tackles the problem of medical image segmentation by designing a new multi-scale network architecture that combines features from different scales to better utilize hierarchical information, aiming to improve performance in segmentation tasks.
Automatic medical image segmentation, an essential component of medical image analysis, plays an importantrole in computer-aided diagnosis. For example, locating and segmenting the liver can be very helpful in livercancer diagnosis and treatment. The state-of-the-art models in medical image segmentation are variants ofthe encoder-decoder architecture such as fully convolutional network (FCN) and U-Net.1A major focus ofthe FCN based segmentation methods has been on network structure engineering by incorporating the latestCNN structures such as ResNet2and DenseNet.3In addition to exploring new network structures for efficientlyabstracting high level features, incorporating structures for multi-scale image feature extraction in FCN hashelped to improve performance in segmentation tasks. In this paper, we design a new multi-scale networkarchitecture, which takes multi-scale inputs with dedicated convolutional paths to efficiently combine featuresfrom different scales to better utilize the hierarchical information.