ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths

Nowadays U-net-like FCNs predominate various biomedical image segmentation applications and attain promising performance, largely due to their elegant architectures, e.g., symmetric contracting and expansive paths as well as lateral skip-connections. It remains a research direction to devise novel architectures to further benefit the segmentation. In this paper, we develop an ACE-net that aims to enhance the feature representation and utilization by augmenting the contracting and expansive paths. In particular, we augment the paths by the recently proposed advanced techniques including ASPP, dense connection and deep supervision mechanisms, and novel connections such as directly connecting the raw image to the expansive side. With these augmentations, ACE-net can utilize features from multiple sources, scales and reception fields to segment while still maintains a relative simple architecture. Experiments on two typical biomedical segmentation tasks validate its effectiveness, where highly competitive results are obtained in both tasks while ACE-net still runs fast at inference.

[1]  Joachim M. Buhmann,et al.  Crowdsourcing the creation of image segmentation algorithms for connectomics , 2015, Front. Neuroanat..

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

[3]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[4]  Sonam Singh,et al.  A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation , 2016, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[5]  Bin Wang,et al.  Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[6]  Maurice Weiler,et al.  Learning Steerable Filters for Rotation Equivariant CNNs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[7]  George Papandreou,et al.  Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.

[8]  Fred A Hamprecht,et al.  Multicut brings automated neurite segmentation closer to human performance , 2017, Nature Methods.

[9]  Albert C. S. Chung,et al.  Deep supervision with additional labels for retinal vessel segmentation task , 2018, MICCAI.

[10]  Matthew B. Blaschko,et al.  A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images , 2017, IEEE Transactions on Biomedical Engineering.

[11]  Sebastian J. Schlecht,et al.  Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks , 2017, ArXiv.

[12]  Yuan Zhang,et al.  Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function , 2018, Neurocomputing.

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

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

[15]  George Azzopardi,et al.  Trainable COSFIRE filters for vessel delineation with application to retinal images , 2015, Medical Image Anal..

[16]  Yanning Zhang,et al.  Multiscale Network Followed Network Model for Retinal Vessel Segmentation , 2018, MICCAI.

[17]  Tianfu Wang,et al.  A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images , 2016, IEEE Transactions on Medical Imaging.

[18]  Gang Yu,et al.  Learning a Discriminative Feature Network for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Won-Ki Jeong,et al.  FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics , 2016, Frontiers in Computer Science.

[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).