Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images

• URL: http://arxiv.org/abs/2110.12192v1
• Date: Sat, 23 Oct 2021 10:53:37 GMT
• Title: Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images
• Authors: Shuai Wang, Theodore Yanagihara, Bhishamjit Chera, Colette Shen, Pew-Thian Yap, Jun Lian
• Abstract summary: We propose a novel dual shape guided network (DSGnet) to automatically delineate nine important organs-at-risk (OARs) in head and neck CT images. To deal with the large shape variation and unclear boundary of OARs in CT images, we represent the organ shape using an organ-specific unilateral inverse-distance map (UIDM) The overall Dice Similarity Coefficient (DSC) value of 0.842 across the nine important OARs demonstrates great potential applications in improving the delineation quality and reducing the time cost.
• Score: 18.96016069277052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accurate segmentation of organs-at-risk (OARs) in head and neck CT images is a critical step for radiation therapy of head and neck cancer patients. However, manual delineation for numerous OARs is time-consuming and laborious, even for expert oncologists. Moreover, manual delineation results are susceptible to high intra- and inter-variability. To this end, we propose a novel dual shape guided network (DSGnet) to automatically delineate nine important OARs in head and neck CT images. To deal with the large shape variation and unclear boundary of OARs in CT images, we represent the organ shape using an organ-specific unilateral inverse-distance map (UIDM) and guide the segmentation task from two different perspectives: direct shape guidance by following the segmentation prediction and across shape guidance by sharing the segmentation feature. In the direct shape guidance, the segmentation prediction is not only supervised by the true label mask, but also by the true UIDM, which is implemented through a simple yet effective encoder-decoder mapping from the label space to the distance space. In the across shape guidance, UIDM is used to facilitate the segmentation by optimizing the shared feature maps. For the experiments, we build a large head and neck CT dataset with a total of 699 images from different volunteers, and conduct comprehensive experiments and comparisons with other state-of-the-art methods to justify the effectiveness and efficiency of our proposed method. The overall Dice Similarity Coefficient (DSC) value of 0.842 across the nine important OARs demonstrates great potential applications in improving the delineation quality and reducing the time cost.

Related papers

• CT-based brain ventricle segmentation via diffusion Schrödinger Bridge without target domain ground truths [0.9720086191214947]
  Efficient and accurate brain ventricle segmentation from clinical CT scans is critical for emergency surgeries like ventriculostomy. We introduce a novel uncertainty-aware ventricle segmentation technique without the need of CT segmentation ground truths. Our method employs the diffusion Schr"odinger Bridge and an attention recurrent residual U-Net to capitalize on unpaired CT and MRI scans.
  arXiv  Detail & Related papers  (2024-05-28T15:17:58Z)
• M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans [25.636974007788986]
  We propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. Our approach demonstrates a considerable performance improvement, achieving an average Dice Similarity Coefficient (DSC) value of up to 89.53% and an Intersection Over Union (IOU) score of up to 81.16 for the NIH pancreas dataset.
  arXiv  Detail & Related papers  (2024-01-18T23:10:08Z)
• 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)
• 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)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• 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)
• Weaving Attention U-net: A Novel Hybrid CNN and Attention-based Method for Organs-at-risk Segmentation in Head and Neck CT Images [11.403827695550111]
  We develop a novel hybrid deep learning approach, combining convolutional neural networks (CNNs) and the self-attention mechanism. We show that the proposed method generated contours that closely resemble the ground truth for ten organs-at-risk (OARs) Our results of the new Weaving Attention U-net demonstrate superior or similar performance on the segmentation of head and neck CT images.
  arXiv  Detail & Related papers  (2021-07-10T14:27:46Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation [73.84166499988443]
  We present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA) Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images.
  arXiv  Detail & Related papers  (2020-02-06T13:49:47Z)

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.