Image Segmentation with Adaptive Spatial Priors from Joint Registration

• URL: http://arxiv.org/abs/2203.15548v1
• Date: Tue, 29 Mar 2022 13:29:59 GMT
• Title: Image Segmentation with Adaptive Spatial Priors from Joint Registration
• Authors: Haifeng Li, Weihong Guo, Jun Liu, Li Cui, and Dongxing Xie
• Abstract summary: In thigh muscle images, different muscles are packed together and there are often no clear boundaries between them. We present a segmentation model with adaptive spatial priors from joint registration. We evaluate our proposed model on synthetic and thigh muscle MR images.
• Score: 10.51970325349652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image segmentation is a crucial but challenging task that has many applications. In medical imaging for instance, intensity inhomogeneity and noise are common. In thigh muscle images, different muscles are closed packed together and there are often no clear boundaries between them. Intensity based segmentation models cannot separate one muscle from another. To solve such problems, in this work we present a segmentation model with adaptive spatial priors from joint registration. This model combines segmentation and registration in a unified framework to leverage their positive mutual influence. The segmentation is based on a modified Gaussian mixture model (GMM), which integrates intensity inhomogeneity and spacial smoothness. The registration plays the role of providing a shape prior. We adopt a modified sum of squared difference (SSD) fidelity term and Tikhonov regularity term for registration, and also utilize Gaussian pyramid and parametric method for robustness. The connection between segmentation and registration is guaranteed by the cross entropy metric that aims to make the segmentation map (from segmentation) and deformed atlas (from registration) as similar as possible. This joint framework is implemented within a constraint optimization framework, which leads to an efficient algorithm. We evaluate our proposed model on synthetic and thigh muscle MR images. Numerical results show the improvement as compared to segmentation and registration performed separately and other joint models.

Related papers

• MsMorph: An Unsupervised pyramid learning network for brain image registration [4.000367245594772]
  MsMorph is an image registration framework aimed at mimicking the manual process of registering image pairs. It decodes semantic information at different scales and continuously compen-sates for the predicted deformation field. The proposed method simulates the manual approach to registration, focusing on different regions of the image pairs and their neighborhoods.
  arXiv  Detail & Related papers  (2024-10-23T19:20:57Z)
• 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)
• Towards Better Certified Segmentation via Diffusion Models [62.21617614504225]
  segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical guarantees. In this paper, we address the problem of certifying segmentation prediction using a combination of randomized smoothing and diffusion models.
  arXiv  Detail & Related papers  (2023-06-16T16:30:39Z)
• Cross-modal tumor segmentation using generative blending augmentation and self training [1.6440045168835438]
  We propose a cross-modal segmentation method based on conventional image synthesis boosted by a new data augmentation technique. Generative Blending Augmentation (GBA) learns representative generative features from a single training image to realistically diversify tumor appearances. The proposed solution ranked first for vestibular schwannoma (VS) segmentation during the validation and test phases of the MICCAI CrossMoDA 2022 challenge.
  arXiv  Detail & Related papers  (2023-04-04T11:01:46Z)
• Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation [13.430851964063534]
  We propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for one-shot segmentation of brain tissues. Experimental results on two public datasets demonstrate 1) a competitive segmentation performance of our method compared to the fully-supervised method, and 2) a superior performance over other state-of-the-art with an increase of average Dice by up to 4.67%.
  arXiv  Detail & Related papers  (2022-11-26T09:14:01Z)
• Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images [74.99415008543276]
  Most deep learning-based registration methods assume that the deformation fields are smooth and continuous everywhere in the image domain. We propose a novel discontinuity-preserving image registration method to tackle this challenge, which ensures globally discontinuous and locally smooth deformation fields. A co-attention block is proposed in the segmentation component of the network to learn the structural correlations in the input images. We evaluate our method on the task of intra-subject-temporal image registration using large-scale cinematic cardiac magnetic resonance image sequences.
  arXiv  Detail & Related papers  (2022-11-24T23:45:01Z)
• Saliency-Driven Active Contour Model for Image Segmentation [2.8348950186890467]
  We propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models.
  arXiv  Detail & Related papers  (2022-05-23T06:02:52Z)
• Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation [51.916106055115755]
  We propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN) Our architecture is composed of three sequential modules that are estimated together during training. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners.
  arXiv  Detail & Related papers  (2021-07-06T14:50:03Z)
• A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing [75.36126971685034]
  We treat alignment and segmentation as latent variables in our model and induce them as part of end-to-end training. Our method also approaches that of a model that relies on citetLyu2018AMRPA's segmentation rules, which were hand-crafted to handle individual AMR constructions.
  arXiv  Detail & Related papers  (2020-10-23T21:22:50Z)
• MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation [14.36896617430302]
  We propose a new image registration framework based on generative model (MvMM) and neural network estimation. A generative model consolidating both appearance and anatomical information is established to derive a novel loss function capable of implementing groupwise registration. We highlight the versatility of the proposed framework for various applications on multimodal cardiac images.
  arXiv  Detail & Related papers  (2020-06-28T11:19:15Z)
• 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)

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.