scholarly journals Assignment Flow for Order-Constrained OCT Segmentation

2021 ◽  
Author(s):  
Dmitrij Sitenko ◽  
Bastian Boll ◽  
Christoph Schnörr
Keyword(s):  
Geometric Approach ◽  
Ground Truth ◽  
Retinal Cell ◽  
Dice Similarity Coefficient ◽  
Retinal Layer ◽  
Healthy Human ◽  
Data Set ◽  
Ground Truth Data ◽  
Global Shape ◽  
Layer Segmentation

AbstractAt 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. The substantial increase of accessible highly resolved 3D samples at the optic nerve head and the macula is directly linked to medical advancements in early detection of eye diseases. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact measurement of retinal layer thicknesses serves as an essential task be done for each patient separately. However, manual examination of OCT scans is a demanding and time consuming task, which is typically made difficult by 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 geometric approach to order-constrained 3D OCT retinal cell layer segmentation which takes as input data in any metric space and can be implemented using only simple, highly parallelizable operations. As opposed to many established retinal layer segmentation methods, we use only locally extracted features as input and do not employ any global shape prior. The physiological order of retinal cell layers and membranes is achieved through the introduction of a smoothed energy term. This is combined with additional regularization of local smoothness to yield highly accurate 3D segmentations. The approach thereby systematically avoid bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To demonstrate its robustness, 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 automatic retinal layer segmention as well as to manually annotated ground truth data in terms of mean absolute error and Dice similarity coefficient. Visualizations of segmented volumes are also provided.

Chemical Proportionality within Molecular Networks

2021 ◽  
Author(s):  
Daniel Petras ◽  
Andrés Mauricio Caraballo-Rodríguez ◽  
Alan K. Jarmusch ◽  
Carlos Molina-Santiago ◽  
Julia M. Gauglitz ◽  
...  
Keyword(s):  
Natural Products ◽  
Ground Truth ◽  
Mass Spectrometry Data ◽  
Molecular Networks ◽  
Data Series ◽  
Sequential Data ◽  
Chemical Transformations ◽  
Data Set ◽  
Molecular Networking ◽  
Ground Truth Data

Molecular networking of non-targeted tandem mass spectrometry data connects structurally related molecules based on similar fragmentation spectra. Here we report the Chemical Proportionality contextualization of molecular networks. ChemProp scores the changes of abundance between two connected nodes over sequential data series which can be displayed as a direction within the network to prioritize potential biological and chemical transformations or proportional changes of related compounds. We tested the ChemProp workflow on a ground truth data set of defined mixture and highlighted the utility of the tool to prioritize specific molecules within biological samples, including bacterial transformations of bile acids, human drug metabolism and bacterial natural products biosynthesis. The ChemProp workflow is freely available through the Global Natural Products Social Molecular Networking environment.<br><b> </b>

scholarly journals Terrestrial Laser Scanning and Ground Truth Data for Characterizing Downed Dead Wood

2020 ◽  
Author(s):  
Tuomas Yrttimaa ◽  
Ninni Saarinen ◽  
Ville Luoma ◽  
Topi Tanhuanpää ◽  
Ville Kankare ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Laser Scanning ◽  
Dead Wood ◽  
Terrestrial Laser Scanning ◽  
Ground Truth ◽  
Point Clouds ◽  
Data Set ◽  
Ground Truth Data ◽  
Standing Trees ◽  
Downed Dead Wood

The feasibility of terrestrial laser scanning (TLS) in characterizing standing trees has been frequently investigated, while less effort has been put in quantifying downed dead wood using TLS. To advance dead wood characterization using TLS, we collected TLS point clouds and downed dead wood information from 20 sample plots (32 m x 32 m in size) located in southern Finland. This data set can be used in developing new algorithms for downed dead wood detection and characterization as well as for understanding spatial patterns of downed dead wood in boreal forests.

scholarly journals Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set

2020 ◽  
Vol 890 (2) ◽  
pp. 103 ◽  
Author(s):  
Shin Toriumi ◽  
Shinsuke Takasao ◽  
Mark C. M. Cheung ◽  
Chaowei Jiang ◽  
Yang Guo ◽  
...  
Keyword(s):  
Comparative Study ◽  
Ground Truth ◽  
Data Driven ◽  
Flux Emergence ◽  
Data Set ◽  
Ground Truth Data ◽  
Coronal Field ◽  
Solar Coronal

A Visual Tracking Scheme for Accurate Object Retrieval in Low Frame Rate Videos

2016 ◽  
Vol 25 (05) ◽  
pp. 1640003 ◽  
Author(s):  
Yoav Liberman ◽  
Adi Perry
Keyword(s):  
Visual Tracking ◽  
Template Matching ◽  
Large Data ◽  
Ground Truth ◽  
Frame Rate ◽  
Data Set ◽  
Ground Truth Data ◽  
Multiple Metrics ◽  
Low Frame Rate ◽  
Tracking Objects

Visual tracking in low frame rate (LFR) videos has many inherent difficulties for achieving accurate target recovery, such as occlusions, abrupt motions and rapid pose changes. Thus, conventional tracking methods cannot be applied reliably. In this paper, we offer a new scheme for tracking objects in low frame rate videos. We present a method of integrating multiple metrics for template matching, as an extension for the particle filter. By inspecting a large data set of videos for tracking, we show that our method not only outperforms other related benchmarks in the field, but it also achieves better results both visually and quantitatively, once compared to actual ground truth data.

Mixture Modeling with Pairwise, Instance-Level Class Constraints

2005 ◽  
Vol 17 (11) ◽  
pp. 2482-2507 ◽  
Author(s):  
Qi Zhao ◽  
David J. Miller
Keyword(s):  
Synthetic Data ◽  
Ground Truth ◽  
Mixture Modeling ◽  
Data Set ◽  
Ground Truth Data ◽  
New Class ◽  
Recent Approach ◽  
Semisupervised Clustering ◽  
The One ◽  
Number Of Classes

The goal of semisupervised clustering/mixture modeling is to learn the underlying groups comprising a given data set when there is also some form of instance-level supervision available, usually in the form of labels or pairwise sample constraints. Most prior work with constraints assumes the number of classes is known, with each learned cluster assumed to be a class and, hence, subject to the given class constraints. When the number of classes is unknown or when the one-cluster-per-class assumption is not valid, the use of constraints may actually be deleterious to learning the ground-truth data groups. We address this by (1) allowing allocation of multiple mixture components to individual classes and (2) estimating both the number of components and the number of classes. We also address new class discovery, with components void of constraints treated as putative unknown classes. For both real-world and synthetic data, our method is shown to accurately estimate the number of classes and to give favorable comparison with the recent approach of Shental, Bar-Hillel, Hertz, and Weinshall (2003).

scholarly journals Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching

2020 ◽  
Vol 13 (4) ◽  
Author(s):  
Ioannis Agtzidis ◽  
Mikhail Startsev ◽  
Michael Dorr
Keyword(s):  
Eye Movement ◽  
Large Scale ◽  
Ground Truth ◽  
Training Set ◽  
Event Type ◽  
Short Article ◽  
Data Set ◽  
Ground Truth Data ◽  
The Gaze ◽  
Movie Clip

In this short article we present our manual annotation of the eye movement events in a subset of the large-scale eye tracking data set Hollywood2. Our labels include fixations, saccades, and smooth pursuits, as well as a noise event type (the latter representing either blinks, loss of tracking, or physically implausible signals). In order to achieve more consistent annotations, the gaze samples were labelled by a novice rater based on rudimentary algorithmic suggestions, and subsequently corrected by an expert rater. Overall, we annotated eye movement events in the recordings corresponding to 50 randomly selected test set clips and 6 training set clips from Hollywood2, which were viewed by 16 observers and amount to a total of approximately 130 minutes of gaze data. In these labels, 62.4% of the samples were attributed to fixations, 9.1% – to saccades, and, notably, 24.2% – to pursuit (the remainder marked as noise). After evaluation of 15 published eye movement classification algorithms on our newly collected annotated data set, we found that the most recent algorithms perform very well on average, and even reach human-level labelling quality for fixations and saccades, but all have a much larger room for improvement when it comes to smooth pursuit classification. The data set is made available at https://gin.g- node.org/ioannis.agtzidis/hollywood2_em.

scholarly journals Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus

2020 ◽  
pp. 1-8
Author(s):  
Jennifer L. Quon ◽  
Michelle Han ◽  
Lily H. Kim ◽  
Mary Ellen Koran ◽  
Leo C. Chen ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Cerebral Ventricle ◽  
Dice Similarity Coefficient ◽  
Study Cohort ◽  
Mr Images ◽  
Data Set ◽  
Ground Truth Data ◽  
Volume Calculation ◽  
Pediatric Hydrocephalus ◽  
Ventricle Segmentation

OBJECTIVEImaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals.METHODSThe study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as “ground truth” data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software.RESULTSModel segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan).CONCLUSIONSThe authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.

A New Ground Truth Data Set For Seismic Studies

2009 ◽  
Vol 80 (3) ◽  
pp. 465-472 ◽  
Author(s):  
I. Bondar ◽  
K. L. McLaughlin
Keyword(s):  
Ground Truth ◽  
Data Set ◽  
Ground Truth Data

scholarly journals Optimizing sparse RFI prediction using deep learning

2019 ◽  
Vol 488 (2) ◽  
pp. 2605-2615 ◽  
Author(s):  
Joshua Kerrigan ◽  
Paul La Plante ◽  
Saul Kohn ◽  
Jonathan C Pober ◽  
James Aguirre ◽  
...  
Keyword(s):  
Ground Truth ◽  
Maximum Amount ◽  
Limiting Factor ◽  
Radio Frequency Interference ◽  
Identification Algorithm ◽  
Radio Telescopes ◽  
Data Set ◽  
Ground Truth Data ◽  
Time Frequency ◽  
Data Throughput

ABSTRACT Radio frequency interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array (HERA) grow larger in number of receivers. To address this, we present a deep fully convolutional neural network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known ‘ground truth’ data set for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6 × 105 HERA time-ordered 1024 channelled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time–frequency context which increases discrimination between RFI and non-RFI. The inclusion of phase when predicting achieves a recall of 0.81, precision of 0.58, and F2 score of 0.75 as applied to our HERA-67 observations.

scholarly journals Automatic choroidal segmentation in OCT images using supervised deep learning methods

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jason Kugelman ◽  
David Alonso-Caneiro ◽  
Scott A. Read ◽  
Jared Hamwood ◽  
Stephen J. Vincent ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Retinal Layer ◽  
Learning Methods ◽  
Cross Sectional ◽  
Physiological Processes ◽  
Analysis Technique ◽  
Layer Segmentation

Abstract The analysis of the choroid in the eye is crucial for our understanding of a range of ocular diseases and physiological processes. Optical coherence tomography (OCT) imaging provides the ability to capture highly detailed cross-sectional images of the choroid yet only a very limited number of commercial OCT instruments provide methods for automatic segmentation of choroidal tissue. Manual annotation of the choroidal boundaries is often performed but this is impractical due to the lengthy time taken to analyse large volumes of images. Therefore, there is a pressing need for reliable and accurate methods to automatically segment choroidal tissue boundaries in OCT images. In this work, a variety of patch-based and fully-convolutional deep learning methods are proposed to accurately determine the location of the choroidal boundaries of interest. The effect of network architecture, patch-size and contrast enhancement methods was tested to better understand the optimal architecture and approach to maximize performance. The results are compared with manual boundary segmentation used as a ground-truth, as well as with a standard image analysis technique. Results of total retinal layer segmentation are also presented for comparison purposes. The findings presented here demonstrate the benefit of deep learning methods for segmentation of the chorio-retinal boundary analysis in OCT images.

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