From Large to Small Organ Segmentation in CT Using Regional Context

Bieth, Marie; Alberts, Esther; Schwaiger, Markus; Menze, Bjoern

doi:10.1007/978-3-319-67389-9_1

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Among registration-free methods, computational power and data availability have enabled the rise of machine learning techniques via voxel- [5], [18], patch- [19] or supervoxel-wise [20] classifiers. These methods require hand-crafted features and therefore, specialized knowledge to delineate anatomical structures.…”

Section: Related Workmentioning

confidence: 99%

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks

Conze¹,

Kavur²,

Gall³

et al. 2020

Preprint

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Objective : Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fullyautomated 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 pretrained 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.

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