MIRD-Net for Medical Image Segmentation

Medical image segmentation is a fundamental and challenging problem for analyzing medical images due to the approximate pixel values of adjacent tissues in boundary and the non-linear feature between pixels. Although fully convolutional neural networks such as U-Net has demonstrated impressive performance on medical image segmentation, distinguishing subtle features between different categories after pooling layers is still a difficult task, which affects the segmentation accuracy. In this paper, we propose a Mini-Inception-Residual-Dense (MIRD) network named MIRD-Net to deal with this problem. The key point of our proposed MIRD-Net is MIRD Block. It takes advantage of Inception, Residual Block (RB) and Dense Block (DB), aiming to make the network obtain more features to help improve the segmentation accuracy. There is no pooling layer in MIRD-Net. Such a design avoids loss of information during forward propagation. Experimental results show that our framework significantly outperforms U-Net in six different image segmentation tasks and its parameters are only about 1/50 of U-Net.

[1]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[2]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Chi-Wing Fu,et al.  H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes , 2018, IEEE Transactions on Medical Imaging.

[4]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Song Wang,et al.  Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting , 2016, LABELS/DLMIA@MICCAI.

[6]  Christopher Joseph Pal,et al.  The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.

[7]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[9]  Vibhav Vineet,et al.  Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  Steven Guan,et al.  Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal , 2018, IEEE Journal of Biomedical and Health Informatics.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[13]  Lisa Tang,et al.  Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation , 2016, IEEE Transactions on Medical Imaging.

[14]  Linda G. Shapiro,et al.  Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images , 2018, MICCAI.

[15]  Dipti Prasad Mukherjee,et al.  Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images , 2015, IEEE Transactions on Image Processing.

[16]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[17]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[18]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[19]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[20]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[21]  Yoshua Bengio,et al.  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[22]  Bostjan Likar,et al.  Model-Based Segmentation of Vertebral Bodies from MR Images with 3D CNNs , 2016, MICCAI.

[23]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[24]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.