Constrained Multi-scale Dense Connections for Accurate Biomedical Image Segmentation

Biomedical image segmentation plays a critical role in clinical diagnosis and medical intervention. Recently, a variety of deep neural networks have boosted the biomedical image segmentation performance with a large margin, which adopts dense connections to explore rich representations in multiple scales. In multi-scale dense connections, features from all or most scales are fused or iteratively aggregated. In this paper, we propose constrained multi-scale dense connections (CMDC) for accurate biomedical image segmentation, which only fuse features from the nearest scales containing the most relevant appearance or semantic information. Based on CMDC, we further construct constraint multi-scale dense networks (CMD-Net) by applying CMDC to existing segmentation networks. Experiments across various architectures (including FCN-8s, U-Net, and DeepLabV3) and datasets (including GlaS, CRAG, KID, and ECS) demonstrate that CMD-Net not only outperforms existing schemes on both accuracy and efficiency but also can be easily generalized to a variety of segmentation networks. In addition, CMD-Net achieves state-of-the-art performance on two instance segmentation datasets, GlaS and CRAG.

[1]  Piotr Bilinski,et al.  Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Yanchun Zhang,et al.  Multi-Stage Attention-Unet for Wireless Capsule Endoscopy Image Bleeding Area Segmentation , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[4]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[5]  Dimitris N. Metaxas,et al.  Quantized Densely Connected U-Nets for Efficient Landmark Localization , 2018, ECCV.

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

[7]  W. Marsden.  I and J , 2012 .

[8]  Hao Chen,et al.  Gland segmentation in colon histology images: The glas challenge contest , 2016, Medical Image Anal..

[9]  Lin Yang,et al.  Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation , 2017, MICCAI.

[10]  Bart Smeets,et al.  Deep Learning-Based Histopathologic Assessment of Kidney Tissue. , 2019, Journal of the American Society of Nephrology : JASN.

[11]  Liping Zheng,et al.  Automatic segmentation of esophageal cancer pathological sections based on semantic segmentation , 2018, 2018 International Conference on Orange Technologies (ICOT).

[12]  S. N. Merchant,et al.  MS-Net: Mixed-Supervision Fully-Convolutional Networks for Full-Resolution Segmentation , 2018, MICCAI.

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

[14]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[15]  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.

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

[17]  Ian D. Reid,et al.  RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[20]  Dong Liu,et al.  Deep High-Resolution Representation Learning for Human Pose Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Farida Cheriet,et al.  A Multitask Learning Architecture for Simultaneous Segmentation of Bright and Red Lesions in Fundus Images , 2018, MICCAI.

[22]  Trevor Darrell,et al.  Deep Layer Aggregation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Walter J. Scheirer,et al.  Neuron Segmentation Using Deep Complete Bipartite Networks , 2017, MICCAI.

[24]  Hao Chen,et al.  MILD‐Net: Minimal information loss dilated network for gland instance segmentation in colon histology images , 2018, Medical Image Anal..

[25]  David B. A. Epstein,et al.  MIMO-Net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[26]  Yi Yang,et al.  Attention to Scale: Scale-Aware Semantic Image Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Xin Yang,et al.  A Deep Model with Shape-Preserving Loss for Gland Instance Segmentation , 2018, MICCAI.

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

[30]  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).

[31]  Yanchun Zhang,et al.  MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation , 2018, Health Information Science and Systems.

[32]  Yang Li,et al.  Gland Instance Segmentation by Deep Multichannel Side Supervision , 2016, MICCAI.

[33]  Yang Li,et al.  Gland Instance Segmentation by Deep Multichannel Neural Networks , 2016, ArXiv.