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

arXiv: https://arxiv.org/abs/2001.09521 · HTML: https://mlsanity.com/paper/2001.09521/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [Medical Image Segmentation with Domain Adaptation: A Survey](https://mlsanity.com/paper/2311.01702/) (2023)
- [Cross-dimensional transfer learning in medical image segmentation with deep learning](https://mlsanity.com/paper/2307.15872/) (2023)
- [A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2309.03906/) (2023)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentation](https://mlsanity.com/paper/2403.07303/) (2024)
- [Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists](https://mlsanity.com/paper/2504.00862/) (2025)
- [On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2202.02764/) (2022)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels](https://mlsanity.com/paper/2403.15609/) (2024)
- [Deep-learning Segmentation of Small Volumes in CT images for Radiotherapy Treatment Planning](https://mlsanity.com/paper/2404.04202/) (2024)
- [Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation](https://mlsanity.com/paper/2409.12333/) (2024)
- [Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation](https://mlsanity.com/paper/2409.13001/) (2024)
- [Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive](https://mlsanity.com/paper/2507.13901/) (2025)

## Abstract (arXiv)

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.