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FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net

Jafari, Mina; Li, Ruizhe; Xing, Yue; Auer, Dorothee; Francis, Susan; Garibaldi, Jonathan; Chen, Xin

Authors

Mina Jafari

Ruizhe Li

Yue Xing

Dorothee Auer

Susan Francis

Jonathan Garibaldi

Xin Chen



Abstract

© 2019, Springer Nature Switzerland AG. In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model, known as U-net. U-net is firstly modified by adding widely used batch normalization and residual block (named as BRU-net) to improve the efficiency of model training. Based on BRU-net, we further introduce a dynamically weighted cross-entropy loss function. The weighting scheme is calculated based on the pixel-wise prediction accuracy during the training process. Assigning higher weights to pixels with lower segmentation accuracies enables the network to learn more from poorly predicted image regions. Our method is named as feedback weighted U-net (FU-net). We have evaluated our method based on T1-weighted brain MRI for the segmentation of midbrain and substantia nigra, where the number of pixels in each class is extremely unbalanced to each other. Based on the dice coefficient measurement, our proposed FU-net has outperformed BRU-net and U-net with statistical significance, especially when only a small number of training examples are available. The code is publicly available in GitHub (GitHub link: https://github.com/MinaJf/FU-net).

Publication Date 2019
Publisher Springer Verlag
Pages 529-537
Series Title Lecture Notes in Computer Science
Series Number 11902
Book Title Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23–25, 2019, Proceedings, Part II
ISBN 9783030341091; 9783030341107
APA6 Citation Jafari, M., Li, R., Xing, Y., Auer, D., Francis, S., Garibaldi, J., & Chen, X. (2019). FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net. In Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23–25, 2019, Proceedings, Part II, (529-537). Springer Verlag. https://doi.org/10.1007/978-3-030-34110-7_44
DOI https://doi.org/10.1007/978-3-030-34110-7_44
Additional Information First Online: 28 November 2019; Conference Acronym: ICIG; Conference Name: International Conference on Image and Graphics; Conference City: Beijing; Conference Country: China; Conference Year: 2019; Conference Start Date: 23 August 2019; Conference End Date: 25 August 2019; Conference Number: 10; Conference ID: icig2019; Conference URL: http://www.csig.org.cn/detail/2669
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