Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration

• URL: http://arxiv.org/abs/2109.02722v1
• Date: Mon, 6 Sep 2021 20:16:27 GMT
• Title: Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration
• Authors: Monika Grewal, Jan Wiersma, Henrike Westerveld, Peter A. N. Bosman, Tanja Alderliesten
• Abstract summary: DCNN-Match learns to predict landmark correspondences in 3D images in a self-supervised manner. Results show significant improvement in DIR performance when landmark correspondences predicted by DCNN-Match were used in case of simulated as well as clinical deformations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deformable Image Registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic detection methods for corresponding landmarks in three-dimensional (3D) medical images. In this work, we present a Deep Convolutional Neural Network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner. We explored five variants of DCNN-Match that use different loss functions and tested DCNN-Match separately as well as in combination with the open-source registration software Elastix to assess its impact on a common DIR approach. We employed lower-abdominal Computed Tomography (CT) scans from cervical cancer patients: 121 pelvic CT scan pairs containing simulated elastic transformations and 11 pairs demonstrating clinical deformations. Our results show significant improvement in DIR performance when landmark correspondences predicted by DCNN-Match were used in case of simulated as well as clinical deformations. We also observed that the spatial distribution of the automatically identified landmarks and the associated matching errors affect the extent of improvement in DIR. Finally, DCNN-Match was found to generalize well to Magnetic Resonance Imaging (MRI) scans without requiring retraining, indicating easy applicability to other datasets.

Related papers

• SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging [59.78761085714715]
  This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework for liver lesion classification. The proposed framework has been validated through comprehensive experiments on two clinical datasets. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public.
  arXiv  Detail & Related papers  (2024-02-27T06:32:56Z)
• X-Ray to CT Rigid Registration Using Scene Coordinate Regression [1.1687067206676627]
  This paper proposes a fully automatic registration method that is robust to extreme viewpoints. It is based on a fully convolutional neural network (CNN) that regresses the overlapping coordinates for a given X-ray image. The proposed method achieved an average mean target registration error (mTRE) of 3.79 mm in the 50th percentile of the simulated test dataset and projected mTRE of 9.65 mm in the 50th percentile of real fluoroscopic images for pelvis registration.
  arXiv  Detail & Related papers  (2023-11-25T17:48:46Z)
• On the Localization of Ultrasound Image Slices within Point Distribution Models [84.27083443424408]
  Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US) Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. We present a framework for automated US image slice localization within a 3D shape representation.
  arXiv  Detail & Related papers  (2023-09-01T10:10:46Z)
• Meta-Learning Initializations for Interactive Medical Image Registration [0.18750851274087482]
  This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application. Applying sparsely sampled data to non-interactive methods yields higher registration errors (6.26 mm), demonstrating the effectiveness of interactive MR-TRUS registration.
  arXiv  Detail & Related papers  (2022-10-27T12:30:53Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• ACSGRegNet: A Deep Learning-based Framework for Unsupervised Joint Affine and Diffeomorphic Registration of Lumbar Spine CT via Cross- and Self-Attention Fusion [4.068962439293273]
  This study proposes a novel end-to-end deep learning-based framework for medical image registration. ACSGRegNet integrates a cross-attention module for establishing inter-image feature correspondences and a self-attention module for intra-image anatomical structures aware. Our method achieved an average Dice of 0.963 and an average distance error of 0.321mm, which are better than the state-of-the-art (SOTA)
  arXiv  Detail & Related papers  (2022-08-04T13:13:48Z)
• 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)
• Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis [3.5718941645696485]
  Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging. We present an un-supervised deep learning registration methodology which can accurately model affine and non-rigid trans-formations. Our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent rep-resentations.
  arXiv  Detail & Related papers  (2022-03-07T12:54:33Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.