Cross-Domain Segmentation of Fundus Vessels Based on Feature Space Alignment

The accurate segmentation of fundus vessels plays a very important role in the detection and treatment of fundus diseases. With the rapid development of Convolutional Neural Networks (CNN), some CNN-based methods have been proposed for the segmentation of fundus vessels which show a good segmentation performance, but they rely on much well-annotated data sets. Aimed at this issue, based on a small number of annotated images, a new segmentation network is proposed in this paper to realize the segmentation of fundus vessels in the cross-domain. Two different high-level feature space are aligned and the Wasserstein distance is used to train the antagonistic networks. Experiments show that the proposed method could acquire a good segmentation performance on the public data sets of the DRIVE and STARE data sets.

[1]  Nico Karssemeijer,et al.  Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation , 2017, MICCAI.

[2]  Klaus H. Maier-Hein,et al.  DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images , 2024, IEEE Transactions on Medical Imaging.

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

[4]  Faisal Mahmood,et al.  Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training , 2017, IEEE Transactions on Medical Imaging.

[5]  Luc Van Gool,et al.  Wasserstein Divergence for GANs , 2017, ECCV.

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

[7]  Joseph O. Deasy,et al.  Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation , 2018, MICCAI.

[8]  Xiangyu Zhang,et al.  Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Bo Du,et al.  Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation , 2019, IEEE Transactions on Medical Imaging.

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

[11]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[12]  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.

[13]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[14]  Krzysztof Krawiec,et al.  Segmenting Retinal Blood Vessels With Deep Neural Networks , 2016, IEEE Transactions on Medical Imaging.

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

[16]  Tolga Tasdizen,et al.  Domain adaptation for biomedical image segmentation using adversarial training , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

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

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