Assignment Flow for Order-Constrained OCT Segmentation

• URL: http://arxiv.org/abs/2009.04632v1
• Date: Thu, 10 Sep 2020 01:57:53 GMT
• Title: Assignment Flow for Order-Constrained OCT Segmentation
• Authors: D. Sitenko, B. Boll, C. Schn\"orr
• Abstract summary: The identification of retinal layer thicknesses serves as an essential task be done for each patient separately. The elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven textitgeometric approach to order-constrained 3D OCT retinal cell layer segmentation
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: At the present time Optical Coherence Tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact identification of retinal layer thicknesses serves as an essential task be done for each patient separately. However, the manual examination of multiple OCT scans in a row is a demanding and time consuming task, which results in a lengthy qualification process and is frequently confounded in the presence of tissue-dependent speckle noise. Therefore, the elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven \textit{geometric approach to order-constrained 3D OCT retinal cell layer segmentation} which takes as input data in any metric space and comes along with basic operations that can be effectively computed in parallel. As opposed to many established retina detection methods, our presented formulation avoids the use of any shape prior and accomplishes the natural order of the retina in a purely geometric way. This makes the approach unbiased and hence suited for the detection of local anatomical changes of retinal tissue structure. To demonstrate robustness of the proposed approach, we compare two different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in terms of mean absolute error and the Dice similarity coefficient. The results indicate a great potential for applying our method to the classification of diseased retina and opens a new research direction regarding the joint segmentation of retinal cell layers and blood vessel structures.

Related papers

• Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations [12.571349114534597]
  We present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets.
  arXiv  Detail & Related papers  (2023-06-19T14:01:47Z)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• 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)
• Whole-Body Lesion Segmentation in 18F-FDG PET/CT [11.662584140924725]
  The proposed model is designed on the basis of the joint 2D and 3D nnUNET architecture to predict lesions across the whole body. We evaluate the proposed method in the context of AutoPet Challenge, which measures the lesion segmentation performance in the metrics of dice score, false-positive volume and false-negative volume.
  arXiv  Detail & Related papers  (2022-09-16T10:49:53Z)
• Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs [8.596819713822477]
  We present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal plexuses and 2) a suite of physics-based image augmentations.
  arXiv  Detail & Related papers  (2022-07-22T14:22:22Z)
• SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors [4.2663199451998475]
  We introduce a semi-supervised paradigm into the retinal layer segmentation task. In particular, a novel fully differentiable approach is used for converting surface position regression into a pixel-wise structured segmentation. In parallel, we propose a set of anatomical priors to improve network training when a limited amount of labeled data is available.
  arXiv  Detail & Related papers  (2022-07-01T14:30:59Z)
• 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)
• JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection [1.2250035750661867]
  We present a novel method, named JOINED, for prior guided multi-task learning for joint OD/OC segmentation and fovea detection. Our proposed JOINED pipeline consists of a coarse stage and a fine stage. Experimental results reveal that our proposed JOINED outperforms existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-03-01T13:47:48Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• 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)
• 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.