Edge-Boosted U-Net for 2D Medical Image Segmentation

Automatic medical image segmentation has always been a heated study in computer-assisted diagnosis (CAD). It is a quite challenging task due to the diversity and complexity of medical images. In this paper, we propose an Edge-boosted U-Net (EU-Net) to address the problem of medical image segmentation. The architecture is basically a U-shape network, combined with three main parts: Edge Aggregation Path, Feature Fusion Block, and Feature Attention Block. The Edge Aggregation Path is to extract multilevel edge-relevant information. The Feature Fusion Block is designed to fuse features from different paths. And the Feature Attention Block is embedded in the network to generate more informative feature maps. The collaboration of these three parts effectively boosts the performance of the whole network. We verified the importance of each part by conducting several experiments. Meanwhile, we compared the proposed method with other state-of-the-art methods on three different modalities of public medical image datasets. Our method achieves the superiority with IoU and dice coefficient respectively on all the datasets. Notably, it attains 2% accuracy improvement over other methods on the challenging datasets.

[1]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[2]  Qin Zhang,et al.  An Efficient and Clinical-Oriented 3D Liver Segmentation Method , 2017, IEEE Access.

[3]  Jian Yang,et al.  Selective Kernel Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Guowei Yang,et al.  A Glioma Segmentation Method Using CoTraining and Superpixel-Based Spatial and Clinical Constraints , 2018, IEEE Access.

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

[6]  Gang Sun,et al.  Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[7]  Daniel Rueckert,et al.  Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain , 2014, IEEE Transactions on Medical Imaging.

[8]  Hao Chen,et al.  DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Yao Lu,et al.  RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images , 2019, IEEE Access.

[11]  Venkateswararao Cherukuri,et al.  Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans , 2017, IEEE Transactions on Biomedical Engineering.

[12]  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.

[13]  Pheng-Ann Heng,et al.  CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation , 2019, IPMI.

[14]  Chunhua Shen,et al.  Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[16]  Stefan Jaeger,et al.  Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. , 2014, Quantitative imaging in medicine and surgery.

[17]  Gang Yu,et al.  BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation , 2018, ECCV.

[18]  Loïc Le Folgoc,et al.  Attention U-Net: Learning Where to Look for the Pancreas , 2018, ArXiv.

[19]  H.P. Ng,et al.  Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm , 2006, 2006 IEEE Southwest Symposium on Image Analysis and Interpretation.

[20]  Ebrahim Nasr-Esfahani,et al.  Liver Segmentation in CT Images Using Three Dimensional to Two Dimensional Fully Convolutional Network , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[21]  Samuel Kadoury,et al.  Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases. , 2019, Radiology. Artificial intelligence.

[22]  Noel C. F. Codella,et al.  Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) , 2019, ArXiv.

[23]  C. Lawrence Zitnick,et al.  Fast Edge Detection Using Structured Forests , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[25]  Vijayan K. Asari,et al.  Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks , 2019, ArXiv.

[26]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Jinhui Tang,et al.  Richer Convolutional Features for Edge Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Mohammad Sohel Rahman,et al.  MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation , 2019, Neural Networks.

[29]  Yousef Al-Kofahi,et al.  Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images , 2010, IEEE Transactions on Biomedical Engineering.

[30]  Harald Kittler,et al.  The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018, Scientific Data.