Validation Of Segmentation Algorithms Research Articles

ScLNet: A cornea with scleral lens OCT layers segmentation dataset and new multi-task model

ObjectiveTo develop deep learning methods with high accuracy for segmenting irregular corneas and detecting the tear fluid reservoir (TFR) boundary under the scleral lens. Additionally, this study aims to provide a publicly available cornea with scleral lens OCT dataset, including manually labeled layer masks for training and validation of segmentation algorithms. This study introduces ScLNet, a dataset comprising cornea with Scleral Lens (ScL) optical coherence tomography (OCT) images with layer annotations, and a multi-task network designed to achieve rapid, accurate, automated segmentation of scleral lens with regular and irregular corneas. MethodsWe created a dataset comprising 31,360 OCT images with scleral lens annotations. The network architecture includes an encoder with multi-scale input and a context coding layer, along with two decoders for specific tasks. The primary task focuses on predicting ScL, TFR, and cornea regions, while the auxiliary task, aimed at predicting the boundaries of ScL, TFR, and cornea, enhances feature extraction for the main task. Segmentation results were compared with state-of-the-art methods and evaluated using Dice similarity coefficient (DSC), intersection over union (IoU), Matthews correlation coefficient (MCC), Precision, and Hausdorff distance (HD). ResultsScLNet achieves 98.22 % DSC, 96.50 % IoU, 98.13 % MCC, 98.35 % Precision, and 3.6840 HD (in pixels) in segmenting ScL; 97.78 % DSC, 95.66 % IoU, 97.71 % MCC, 97.70 % Precision, and 3.7838 HD (in pixels) in segmenting TFR; and 99.22 % DSC, 98.45 % IoU, 99.15 % MCC, 99.14 % Precision, and 3.5355 HD (in pixels) in segmenting cornea. The layer interfaces recognized by ScLNet closely align with expert annotations, as evidenced by high IoU scores. Boundary metrics further confirm its effectiveness. ConclusionWe constructed a dataset of corneal OCT images with ScL wearing, which includes regular and irregular cornea patients. The proposed ScLNet achieves high accuracy in extracting ScL, TFR, and corneal layer masks and boundaries from OCT images of the dataset.

Annotated computed tomography coronary angiogram images and associated data of normal and diseased arteries

Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to create virtual models of coronary arteries. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. We provide anonymized CTCA images, voxel-wise annotations and associated data in the form of centrelines, calcification scores and meshes of the coronary lumen in 20 normal and 20 diseased cases. Images were obtained along with patient information with informed, written consent as part of the Coronary Atlas. Cases were classified as normal (zero calcium score with no signs of stenosis) or diseased (confirmed coronary artery disease). Manual voxel-wise segmentations by three experts were combined using majority voting to generate the final annotations. Provided data can be used for a variety of research purposes, such as 3D printing patient-specific models, development and validation of segmentation algorithms, education and training of medical personnel and in-silico analyses such as testing of medical devices.

ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model.

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of retinal vessels in OCTA has been under-studied due to various challenges such as low capillary visibility and high vessel complexity, despite its significance in understanding many vision-related diseases. In addition, there is no publicly available OCTA dataset with manually graded vessels for training and validation of segmentation algorithms. To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCTA SEgmentation dataset (ROSE), which consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level. This dataset with the source code has been released for public access to assist researchers in the community in undertaking research in related topics. Secondly, we introduce a novel split-based coarse-to-fine vessel segmentation network for OCTA images (OCTA-Net), with the ability to detect thick and thin vessels separately. In the OCTA-Net, a split-based coarse segmentation module is first utilized to produce a preliminary confidence map of vessels, and a split-based refined segmentation module is then used to optimize the shape/contour of the retinal microvasculature. We perform a thorough evaluation of the state-of-the-art vessel segmentation models and our OCTA-Net on the constructed ROSE dataset. The experimental results demonstrate that our OCTA-Net yields better vessel segmentation performance in OCTA than both traditional and other deep learning methods. In addition, we provide a fractal dimension analysis on the segmented microvasculature, and the statistical analysis demonstrates significant differences between the healthy control and Alzheimer's Disease group. This consolidates that the analysis of retinal microvasculature may offer a new scheme to study various neurodegenerative diseases.

MR and CT data with multiobserver delineations of organs in the pelvic area-Part of the Gold Atlas project.

We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT). T1- and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset. The dataset has been made publically available to be used for academic purposes, and can be accessed from https://zenodo.org/record/583096. The dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm.

Free breathing myocardial perfusion data sets for performance analysis of motion compensation algorithms.

BackgroundPerfusion quantification by using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) has proved to be a reliable tool for the diagnosis of coronary artery disease that leads to reduced blood flow to the myocardium. The image series resulting from such acquisition usually exhibits a breathing motion that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. Various algorithms have been presented to facilitate such a motion compensation, but the lack of publicly available data sets hinders a proper, reproducible comparison of these algorithms.MaterialFree breathing perfusion MRI series of ten patients considered clinically to have a stress perfusion defect were acquired; for each patient a rest and a stress study was executed. Manual segmentations of the left ventricle myocardium and the right-left ventricle insertion point are provided for all images in order to make a unified validation of the motion compensation algorithms and the perfusion analysis possible. In addition, all the scripts and the software required to run the experiments are provided alongside the data, and to enable interested parties to directly run the experiments themselves, the test bed is also provided as a virtual hard disk.FindingsTo illustrate the utility of the data set two motion compensation algorithms with publicly available implementations were applied to the data and earlier reported results about the performance of these algorithms could be confirmed.ConclusionThe data repository alongside the evaluation test bed provides the option to reliably compare motion compensation algorithms for myocardial perfusion MRI. In addition, we encourage that researchers add their own annotations to the data set, either to provide inter-observer comparisons of segmentations, or to make other applications possible, for example, the validation of segmentation algorithms.

A method of generating synthetic medical image for the validation of segmentation algorithms

Objective To develop a synthetic medical image generation system which can provide test images for the validation of medical image segmentation algorithms.Methods The synthetic image was created based on the deformation of region of interest (ROI) in original clinical images.First the synthetic foreground boundarywas generated by the resampling of the Fourier descriptors of manually segmented foreground boundary in original image.Then all the ROI pixels were divided into 4 categories and their intensities were calculated by texture matching techniques.Results The intracranial hemorrhage image was selected as the original image,and the generated synthetic images were applied to validate the precision and accuracy of multi-threshold segmentation and level set algorithm.Conclusion The proposed system can rapidly generate synthetic images with realistic appearance of clinical cases and well define ground truth foreground boundary.It has strong practicality for quantitative validation of segmentation algorithms. Key words: Medical image segmentation; Validation of image processing; Fourier descriptor; Texture matching