ACNN: a Full Resolution DCNN for Medical Image Segmentation
arXiv:1901.09203
Abstract
Deep Convolutional Neural Networks (DCNNs) are used extensively in medical image segmentation and hence 3D navigation for robot-assisted Minimally Invasive Surgeries (MISs). However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to image segmentation. Atrous convolution is an alternative for the down sampling layer. It increases the receptive field whilst maintains the spatial dimension of feature maps. In this paper, a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers. Furthermore, a new and full resolution DCNN - Atrous Convolutional Neural Network (ACNN), which incorporates cascaded atrous II-blocks, residual learning and Instance Normalization (IN) is proposed. Application results of the proposed ACNN to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image segmentation demonstrate that the proposed ACNN can achieve higher segmentation Intersection over Unions (IoUs) than U-Net and Deeplabv3+, but with reduced trainable parameters.
7 pages, 2 tables, 6 figures
References in corpus (7)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Instance Normalization: The Missing Ingredient for Fast Stylization
- Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
- Dual Path Networks
- Real-time 3D Shape Instantiation for Partially-deployed Stent Segment from a Single 2D Fluoroscopic Image in Robot-assisted Fenestrated Endovascular Aortic Repair
- Normalization in Training U-Net for 2D Biomedical Semantic Segmentation