A Keypoint Detection and Description Network Based on the Vessel Structure for Multi-Modal Retinal Image Registration

• URL: http://arxiv.org/abs/2201.02242v1
• Date: Thu, 6 Jan 2022 20:43:35 GMT
• Title: A Keypoint Detection and Description Network Based on the Vessel Structure for Multi-Modal Retinal Image Registration
• Authors: Aline Sindel (1), Bettina Hohberger (2), Sebastian Fassihi Dehcordi (2), Christian Mardin (2), Robert L\"ammer (2), Andreas Maier (1), Vincent Christlein (1) ((1) Pattern Recognition Lab, FAU Erlangen-N\"urnberg, (2) Department of Ophthalmology, Universit\"atsklinikum Erlangen)
• Abstract summary: Multiple images with different modalities or acquisition times are often analyzed for the diagnosis of retinal diseases. Our method uses a convolutional neural network to extract features of the vessel structure in multi-modal retinal images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ophthalmological imaging utilizes different imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography. Multiple images with different modalities or acquisition times are often analyzed for the diagnosis of retinal diseases. Automatically aligning the vessel structures in the images by means of multi-modal registration can support the ophthalmologists in their work. Our method uses a convolutional neural network to extract features of the vessel structure in multi-modal retinal images. We jointly train a keypoint detection and description network on small patches using a classification and a cross-modal descriptor loss function and apply the network to the full image size in the test phase. Our method demonstrates the best registration performance on our and a public multi-modal dataset in comparison to competing methods.

Related papers

• Disease Classification and Impact of Pretrained Deep Convolution Neural Networks on Diverse Medical Imaging Datasets across Imaging Modalities [0.0]
  This paper investigates the intricacies of using pretrained deep convolutional neural networks with transfer learning across diverse medical imaging datasets. It shows that the use of pretrained models as fixed feature extractors yields poor performance irrespective of the datasets. It is also found that deeper and more complex architectures did not necessarily result in the best performance.
  arXiv  Detail & Related papers  (2024-08-30T04:51:19Z)
• Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methods [0.0]
  Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina. It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness.
  arXiv  Detail & Related papers  (2023-04-29T16:11:34Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks [2.968191199408213]
  This paper proposes a segmentation-guided domain-adaptation method to adapt images from multiple devices into single image domain. It avoids the time consumption of manual labelling for the upcoming new dataset and the re-training of the existing network.
  arXiv  Detail & Related papers  (2022-08-31T05:06:00Z)
• Multi-modal Retinal Image Registration Using a Keypoint-Based Vessel Structure Aligning Network [9.988115865060589]
  We propose an end-to-end trainable deep learning method for multi-modal retinal image registration. Our method extracts convolutional features from the vessel structure for keypoint detection and description. The keypoint detection and description network and graph neural network are jointly trained in a self-supervised manner.
  arXiv  Detail & Related papers  (2022-07-21T14:36:51Z)
• Multi-modal Aggregation Network for Fast MR Imaging [85.25000133194762]
  We propose a novel Multi-modal Aggregation Network, named MANet, which is capable of discovering complementary representations from a fully sampled auxiliary modality. In our MANet, the representations from the fully sampled auxiliary and undersampled target modalities are learned independently through a specific network. Our MANet follows a hybrid domain learning framework, which allows it to simultaneously recover the frequency signal in the $k$-space domain.
  arXiv  Detail & Related papers  (2021-10-15T13:16:59Z)
• Semantic segmentation of multispectral photoacoustic images using deep learning [53.65837038435433]
  Photoacoustic imaging has the potential to revolutionise healthcare. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. We present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images.
  arXiv  Detail & Related papers  (2021-05-20T09:33:55Z)
• Patch-based field-of-view matching in multi-modal images for electroporation-based ablations [0.6285581681015912]
  Multi-modal imaging sensors are currently involved at different steps of an interventional therapeutic work-flow. Merging this information relies on a correct spatial alignment of the observed anatomy between the acquired images. We show that a regional registration approach using voxel patches provides a good structural compromise between the voxel-wise and "global shifts" approaches.
  arXiv  Detail & Related papers  (2020-11-09T11:27:45Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

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.