Multiple Organs Segmentation in Abdomen CT Scans Using a Cascade of CNNs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11751)


Automatic organ segmentation is a vital prerequisite of many clinical application in radiology. The anatomical variability of organs in the abdomen makes it difficult for many methods to obtain good segmentations for all organs. In this paper, we present a particular ensemble of convolutional neural networks, combining technologies that analyze the images with either a local or a global perspective. In particular, we implemented a cascade of models combining the advantages of using local and global processing. We have evaluated our proposed system on CT scan of 30 subjects in a nested cross-validation framework, showing a significant performance improvement if compared with state-of-the-art methods.


Deep learning Ensemble learning Convolutional neural networks Medical imaging Segmentation Abdomen organs 


  1. 1.
    Aslani, S., et al.: Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. NeuroImage 196, 1–15 (2019)CrossRefGoogle Scholar
  2. 2.
    Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)CrossRefGoogle Scholar
  3. 3.
    Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 11–19 (2017)Google Scholar
  4. 4.
    Karasawa, K., et al.: Multi-atlas pancreas segmentation: atlas selection based on vessel structure. Med. Image Anal. 39, 18–28 (2017)CrossRefGoogle Scholar
  5. 5.
    Landman, B., et al.: Multi-modal learning from unpaired images: application to multi-organ segmentation in CT and MRI. In: 2015 MICCAI Multi-Atlas Labeling Beyond the Cranial Vault - Workshop and Challenge (2015)
  6. 6.
    Larsson, M., Zhang, Y., Kahl, F.: Robust abdominal organ segmentation using regional convolutional neural networks. Appl. Soft Comput. 70, 465–471 (2018)CrossRefGoogle Scholar
  7. 7.
    Pawlowski, N., et al.: DLTK: state of the art reference implementations for deep learning on medical images. In: Medical Imaging Meet NIPS Workshop (2017)Google Scholar
  8. 8.
    Tong, T., et al.: Discriminative dictionary learning for abdominal multi-organ segmentation. Med. Image Anal. 23(1), 92–104 (2015)CrossRefGoogle Scholar
  9. 9.
    Valindria, V.V., et al.: Multi-modal learning from unpaired images: application to multi-organ segmentation in CT and MRI. In: IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2018)Google Scholar
  10. 10.
    Wang, Z., et al.: Geodesic patch-based segmentation. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 666–673. Springer, Cham (2014). Scholar
  11. 11.
    Wimmer, A., Soza, G., Hornegger, J.: A generic probabilistic active shape model for organ segmentation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 26–33. Springer, Heidelberg (2009). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Pattern Analysis and Computer VisionIstituto Italiano di TecnologiaGenovaItaly
  2. 2.Department of Electrical, Electronics and Telecommunication Engineering and Naval ArchitectureUniversità degli Studi di GenovaGenoaItaly
  3. 3.Department of Computer ScienceUniversità di VeronaVeronaItaly
  4. 4.Neuroinformatics LaboratoryFondazione Bruno KesslerTrentoItaly

Personalised recommendations