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

• URL: http://arxiv.org/abs/2001.09521v1
• Date: Sun, 26 Jan 2020 21:28:04 GMT
• Title: Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks
• Authors: Pierre-Henri Conze, Ali Emre Kavur, Emilie Cornec-Le Gall, Naciye Sinem Gezer, Yannick Le Meur, M. Alper Selver and Fran\c{c}ois Rousseau
• Abstract summary: We address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. Our pipeline provides promising results by outperforming state-of-the-art encoder-decoder schemes.
• Score: 0.36944296923226316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery [3.079885946230076]
  State-of-the-art segmentation models often lead to organ hallucinations, i.e., false-positive predictions of organs. We propose HALOS for abdominal organ segmentation in MR images that handles cases after organ resection surgery.
  arXiv  Detail & Related papers  (2023-03-14T09:05:19Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Learning from partially labeled data for multi-organ and tumor segmentation [102.55303521877933]
  We propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple datasets. A dynamic head enables the network to accomplish multiple segmentation tasks flexibly. We create a large-scale partially labeled Multi-Organ and Tumor benchmark, termed MOTS, and demonstrate the superior performance of our TransDoDNet over other competitors.
  arXiv  Detail & Related papers  (2022-11-13T13:03:09Z)
• Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks [9.416108287575915]
  We train the 3D encoder-decoder network to simultaneously segment the abdominal organs and their corresponding boundaries in CT scans. We evaluate the utilization of complementary boundary prediction task in improving the abdominal multi-organ segmentation.
  arXiv  Detail & Related papers  (2022-10-09T15:31:19Z)
• Boundary-Aware Network for Abdominal Multi-Organ Segmentation [21.079667938055668]
  We propose a boundary-aware network (BA-Net) to segment abdominal organs on CT scans and MRI scans. The results demonstrate that BA-Net is superior to nnUNet on both segmentation tasks.
  arXiv  Detail & Related papers  (2022-08-29T02:24:02Z)
• BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images [83.82141604007899]
  BCS-Net is a novel network for automatic COVID-19 lung infection segmentation from CT images. BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder.
  arXiv  Detail & Related papers  (2022-07-17T08:54:07Z)
• Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation [13.544665065396373]
  We propose a 3D network with four main components trained end-to-end including encoder, edge detector, decoder with edge skip-connections and recurrent feature propagation head. Experimental results show that the proposed network outperforms several state-of-the-art models.
  arXiv  Detail & Related papers  (2022-01-02T08:33:19Z)
• Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration [12.861503169117208]
  Multimodal image registration has many applications in diagnostic medical imaging and image-guided interventions. The ability to register peri-procedurally acquired diagnostic images into the intraprocedural environment can potentially improve the intra-procedural tumor targeting. We propose an anatomy-preserving domain adaptation to segmentation network (APA2Seg-Net) for learning segmentation without target modality ground truth.
  arXiv  Detail & Related papers  (2021-04-14T18:07:03Z)
• Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation [55.1248480381153]
  We propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. We show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task.
  arXiv  Detail & Related papers  (2021-03-30T13:41:12Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.