Microvasculature Segmentation and Inter-capillary Area Quantification of
the Deep Vascular Complex using Transfer Learning
- URL: http://arxiv.org/abs/2003.09033v1
- Date: Thu, 19 Mar 2020 22:27:02 GMT
- Title: Microvasculature Segmentation and Inter-capillary Area Quantification of
the Deep Vascular Complex using Transfer Learning
- Authors: Julian Lo (1), Morgan Heisler (1), Vinicius Vanzan (2), Sonja Karst (2
and 3), Ivana Zadro Matovinovic (4), Sven Loncaric (4), Eduardo V. Navajas
(2), Mirza Faisal Beg (1), Marinko V. Sarunic (1) ((1) School of Engineering
Science, Simon Fraser University, Canada, (2) Department of Ophthalmology and
Visual Sciences, University of British Columbia, Canada, (3) Department of
Ophthalmology and Optometry, Medical University of Vienna, Austria, (4)
Faculty of Electrical Engineering and Computing, University of Zagreb,
Croatia)
- Abstract summary: We demonstrate accurate segmentation of the superficial superficial vascular complex and deep vascular plexus using a convolutional neural network (CNN) for quantitative analysis.
We used transfer learning from a CNN trained on 76 images from smaller FOVs of the SCP acquired using different OCT systems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Purpose: Optical Coherence Tomography Angiography (OCT-A) permits
visualization of the changes to the retinal circulation due to diabetic
retinopathy (DR), a microvascular complication of diabetes. We demonstrate
accurate segmentation of the vascular morphology for the superficial capillary
plexus and deep vascular complex (SCP and DVC) using a convolutional neural
network (CNN) for quantitative analysis.
Methods: Retinal OCT-A with a 6x6mm field of view (FOV) were acquired using a
Zeiss PlexElite. Multiple-volume acquisition and averaging enhanced the vessel
network contrast used for training the CNN. We used transfer learning from a
CNN trained on 76 images from smaller FOVs of the SCP acquired using different
OCT systems. Quantitative analysis of perfusion was performed on the automated
vessel segmentations in representative patients with DR.
Results: The automated segmentations of the OCT-A images maintained the
hierarchical branching and lobular morphologies of the SCP and DVC,
respectively. The network segmented the SCP with an accuracy of 0.8599, and a
Dice index of 0.8618. For the DVC, the accuracy was 0.7986, and the Dice index
was 0.8139. The inter-rater comparisons for the SCP had an accuracy and Dice
index of 0.8300 and 0.6700, respectively, and 0.6874 and 0.7416 for the DVC.
Conclusions: Transfer learning reduces the amount of manually-annotated
images required, while producing high quality automatic segmentations of the
SCP and DVC. Using high quality training data preserves the characteristic
appearance of the capillary networks in each layer.
Translational Relevance: Accurate retinal microvasculature segmentation with
the CNN results in improved perfusion analysis in diabetic retinopathy.
Related papers
- OCTolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) data [3.8485899972356337]
OCTolyzer is an open-source toolkit for retinochoroidal analysis in optical coherence tomography ( OCT) and scanning laser ophthalmoscopy (SLO) images.
arXiv Detail & Related papers (2024-07-19T08:56:12Z) - TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images [62.53931644063323]
In this study we extended the capabilities of TotalSegmentator to MR images.
We trained an nnU-Net segmentation algorithm on this dataset and calculated similarity coefficients (Dice) to evaluate the model's performance.
The model significantly outperformed two other publicly available segmentation models (Dice score 0.824 versus 0.762; p0.001 and 0.762 versus 0.542; p)
arXiv Detail & Related papers (2024-05-29T20:15:54Z) - Automatic Segmentation of the Kidneys and Cystic Renal Lesions on Non-Contrast CT Using a Convolutional Neural Network [0.1398098625978622]
Prior automated segmentation models have largely ignored non-contrast computed tomography (CT) imaging.
This work aims to implement and train a deep learning (DL) model to segment the kidneys and cystic renal lesions (CRLs) from non-contrast CT scans.
arXiv Detail & Related papers (2024-05-14T02:34:56Z) - Attention-based Saliency Maps Improve Interpretability of Pneumothorax
Classification [52.77024349608834]
To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency.
ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData.
ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
arXiv Detail & Related papers (2023-03-03T12:05:41Z) - Kidney and Kidney Tumour Segmentation in CT Images [0.0]
This study focuses on the development of an approach for automatic kidney and kidney tumour segmentation in contrast-enhanced CT images.
A 3D U-Net segmentation model was developed and trained to delineate the kidney and kidney tumour from CT scans.
For testing, the model obtained a kidney Dice score of 0.8034, and a kidney tumour Dice score of 0.4713, with an average Dice score of 0.6374.
arXiv Detail & Related papers (2022-12-26T08:08:44Z) - 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) - Data-Efficient Vision Transformers for Multi-Label Disease
Classification on Chest Radiographs [55.78588835407174]
Vision Transformers (ViTs) have not been applied to this task despite their high classification performance on generic images.
ViTs do not rely on convolutions but on patch-based self-attention and in contrast to CNNs, no prior knowledge of local connectivity is present.
Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.
arXiv Detail & Related papers (2022-08-17T09:07:45Z) - Segmentation-free PVC for Cardiac SPECT using a Densely-connected
Multi-dimensional Dynamic Network [11.546783296332961]
Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective.
In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation.
arXiv Detail & Related papers (2022-06-24T15:31:14Z) - 3D Structural Analysis of the Optic Nerve Head to Robustly Discriminate
Between Papilledema and Optic Disc Drusen [44.754910718620295]
We developed a deep learning algorithm to identify major tissue structures of the optic nerve head (ONH) in 3D optical coherence tomography ( OCT) scans.
A classification algorithm was designed using 150 OCT volumes to perform 3-class classifications (1: ODD, 2: papilledema, 3: healthy) strictly from their drusen and prelamina swelling scores.
Our AI approach accurately discriminated ODD from papilledema, using a single OCT scan.
arXiv Detail & Related papers (2021-12-18T17:05:53Z) - Multi-Task Neural Networks with Spatial Activation for Retinal Vessel
Segmentation and Artery/Vein Classification [49.64863177155927]
We propose a multi-task deep neural network with spatial activation mechanism to segment full retinal vessel, artery and vein simultaneously.
The proposed network achieves pixel-wise accuracy of 95.70% for vessel segmentation, and A/V classification accuracy of 94.50%, which is the state-of-the-art performance for both tasks.
arXiv Detail & Related papers (2020-07-18T05:46:47Z) - Automated segmentation of retinal fluid volumes from structural and
angiographic optical coherence tomography using deep learning [2.041049231600541]
We proposed a deep convolutional neural network (CNN) named Retinal Fluid Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography ( OCT) volume.
Cross-sectional OCT and angiography ( OCTA) scans were used for training and testing ReF-Net.
ReF-Net shows high accuracy (F1 = 0.864 +/- 0.084) in retinal fluid segmentation.
arXiv Detail & Related papers (2020-06-03T22:55:47Z)
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.