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Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data

  • Abhijit Guha RoyEmail author
  • Sailesh Conjeti
  • Debdoot Sheet
  • Amin Katouzian
  • Nassir Navab
  • Christian Wachinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited. We propose to automatically create auxiliary labels on initially unlabeled data with existing tools and to use them for pre-training. For the subsequent fine-tuning of the network with manually labeled data, we introduce error corrective boosting (ECB), which emphasizes parameter updates on classes with lower accuracy. Furthermore, we introduce SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that combines skip connections with the unpooling strategy for upsampling. The SD-Net addresses challenges of severe class imbalance and errors along boundaries. With application to whole-brain MRI T1 scan segmentation, we generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on two datasets with manual annotations. Our results show that the inclusion of auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D scan in 7 s in comparison to 30 h for the closest multi-atlas segmentation method, while reaching similar performance. It also outperforms the latest state-of-the-art F-CNN models.

Notes

Acknowledgement

This work was supported in part by the Faculty of Medicine at LMU (FöFoLe), the Bavarian State Ministry of Education, Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B), the NVIDIA corporation and DAAD (German Academic Exchange Service). The authors would also like to thank Magdalini Paschali for proof reading and feedback.

Supplementary material

455908_1_En_27_MOESM1_ESM.pdf (70 kb)
Supplementary material 1 (pdf 70 KB)
455908_1_En_27_MOESM2_ESM.pdf (60 kb)
Supplementary material 2 (pdf 60 KB)
455908_1_En_27_MOESM3_ESM.pdf (1.6 mb)
Supplementary material 3 (pdf 1679 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Abhijit Guha Roy
    • 1
    • 2
    • 3
    Email author
  • Sailesh Conjeti
    • 2
  • Debdoot Sheet
    • 3
  • Amin Katouzian
    • 4
  • Nassir Navab
    • 2
    • 5
  • Christian Wachinger
    • 1
  1. 1.Artificial Intelligence in Medical Imaging (AI-Med)KJP, LMU MünchenMunichGermany
  2. 2.Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
  3. 3.Indian Institute of Technology KharagpurKharagpurIndia
  4. 4.IBM Almaden Research CenterAlmadenUSA
  5. 5.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

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