Boundary-Aware Network for Kidney Parsing

• URL: http://arxiv.org/abs/2208.13338v1
• Date: Mon, 29 Aug 2022 02:19:30 GMT
• Title: Boundary-Aware Network for Kidney Parsing
• Authors: Shishuai Hu and Yiwen Ye and Zehui Liao and Yong Xia
• Abstract summary: We propose a boundary-aware network (BA-Net) to segment kidneys on computed tomography angiography (CTA) scans. The model contains a shared encoder, a boundary decoder, and a segmentation decoder. The results demonstrate the effectiveness of the BA-Net.
• Score: 18.75582522299797
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable tumor sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Kidney PArsing (KiPA) Challenge dataset and achieved an average Dice score of 89.65$\%$ for kidney structure segmentation on CTA scans using 4-fold cross-validation. The results demonstrate the effectiveness of the BA-Net.

Related papers

• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers [13.602882723160388]
  The segmentation of kidney layer structures plays an essential role in automated image analysis in renal pathology. The current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches.
  arXiv  Detail & Related papers  (2023-09-05T20:24:27Z)
• 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)
• An evaluation of U-Net in Renal Structure Segmentation [13.7055816814391]
  Kidney PArsing(KiPA 2022) Challenge aims to build a fine-grained multi-structure dataset. We evaluated several U-Net variants and selected the best models for the final submission.
  arXiv  Detail & Related papers  (2022-09-06T06:53:41Z)
• 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)
• CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation [0.9115927248875568]
  We propose a channel extending and axial attention catching Network(CANet) for multi-structure kidney segmentation. We evaluate our CANet on the KiPA2022 dataset, achieving the dice scores of 95.8%, 89.1%, 87.5% and 84.9% for kidney, tumor, artery and vein, respectively.
  arXiv  Detail & Related papers  (2022-08-10T09:49:19Z)
• 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)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)
• Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation [0.8397730500554047]
  We present a multi-scale supervised 3D U-Net, MSS U-Net, to automatically segment kidneys and kidney tumors from CT images. Our architecture combines deep supervision with exponential logarithmic loss to increase the 3D U-Net training efficiency. This architecture shows superior performance compared to state-of-the-art works using data from KiTS19 public dataset.
  arXiv  Detail & Related papers  (2020-04-17T08:25:43Z)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)

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.