  Objective : Abdominal anatomy segmentation is crucial for numerous
applications from computer-assisted diagnosis to image-guided surgery. In this
context, we address fully-automated multi-organ segmentation from abdominal CT
and MR images using deep learning. Methods: The proposed model extends standard
conditional generative adversarial networks. Additionally to the discriminator
which enforces the model to create realistic organ delineations, it embeds
cascaded partially pre-trained convolutional encoder-decoders as generator.
Encoder fine-tuning from a large amount of non-medical images alleviates data
scarcity limitations. The network is trained end-to-end to benefit from
simultaneous multi-level segmentation refinements using auto-context. Results :
Employed for healthy liver, kidneys and spleen segmentation, our pipeline
provides promising results by outperforming state-of-the-art encoder-decoder
schemes. Followed for the Combined Healthy Abdominal Organ Segmentation (CHAOS)
challenge organized in conjunction with the IEEE International Symposium on
Biomedical Imaging 2019, it gave us the first rank for three competition
categories: liver CT, liver MR and multi-organ MR segmentation. Conclusion :
Combining cascaded convolutional and adversarial networks strengthens the
ability of deep learning pipelines to automatically delineate multiple
abdominal organs, with good generalization capability. Significance : The
comprehensive evaluation provided suggests that better guidance could be
achieved to help clinicians in abdominal image interpretation and clinical
decision making.
