scholarly journals LUMINOUS database: lumbar multifidus muscle segmentation from ultrasound images

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Clyde J. Belasso ◽  
Bahareh Behboodi ◽  
Habib Benali ◽  
Mathieu Boily ◽  
Hassan Rivaz ◽  
...  
Keyword(s):  
Ground Truth ◽  
Ease Of Use ◽  
Automated Segmentation ◽  
Manual Segmentation ◽  
Clinical Implementation ◽  
Multifidus Muscle ◽  
Lumbar Multifidus ◽  
The Us ◽  
Segmentation Algorithms ◽  
And Function

Abstract Background Among the paraspinal muscles, the structure and function of the lumbar multifidus (LM) has become of great interest to researchers and clinicians involved in lower back pain and muscle rehabilitation. Ultrasound (US) imaging of the LM muscle is a useful clinical tool which can be used in the assessment of muscle morphology and function. US is widely used due to its portability, cost-effectiveness, and ease-of-use. In order to assess muscle function, quantitative information of the LM must be extracted from the US image by means of manual segmentation. However, manual segmentation requires a higher level of training and experience and is characterized by a level of difficulty and subjectivity associated with image interpretation. Thus, the development of automated segmentation methods is warranted and would strongly benefit clinicians and researchers. The aim of this study is to provide a database which will contribute to the development of automated segmentation algorithms of the LM. Construction and content This database provides the US ground truth of the left and right LM muscles at the L5 level (in prone and standing positions) of 109 young athletic adults involved in Concordia University’s varsity teams. The LUMINOUS database contains the US images with their corresponding manually segmented binary masks, serving as the ground truth. The purpose of the database is to enable development and validation of deep learning algorithms used for automatic segmentation tasks related to the assessment of the LM cross-sectional area (CSA) and echo intensity (EI). The LUMINOUS database is publicly available at http://data.sonography.ai. Conclusion The development of automated segmentation algorithms based on this database will promote the standardization of LM measurements and facilitate comparison among studies. Moreover, it can accelerate the clinical implementation of quantitative muscle assessment in clinical and research settings.

scholarly journals Quantifying Variability of Manual Annotation in Cryo-Electron Tomograms

2016 ◽  
Vol 22 (3) ◽  
pp. 487-496 ◽  
Author(s):  
Corey W. Hecksel ◽  
Michele C. Darrow ◽  
Wei Dai ◽  
Jesús G. Galaz-Montoya ◽  
Jessica A. Chin ◽  
...  
Keyword(s):  
Confidence Level ◽  
Electron Tomography ◽  
Ground Truth ◽  
Automated Segmentation ◽  
Manual Annotation ◽  
Algorithm Development ◽  
Automated Annotation ◽  
Cryo Electron Tomography ◽  
Segmentation Algorithms ◽  
Tomography Data

AbstractAlthough acknowledged to be variable and subjective, manual annotation of cryo-electron tomography data is commonly used to answer structural questions and to create a “ground truth” for evaluation of automated segmentation algorithms. Validation of such annotation is lacking, but is critical for understanding the reproducibility of manual annotations. Here, we used voxel-based similarity scores for a variety of specimens, ranging in complexity and segmented by several annotators, to quantify the variation among their annotations. In addition, we have identified procedures for merging annotations to reduce variability, thereby increasing the reliability of manual annotation. Based on our analyses, we find that it is necessary to combine multiple manual annotations to increase the confidence level for answering structural questions. We also make recommendations to guide algorithm development for automated annotation of features of interest.

scholarly journals Spring Freeze Damage of Pecan Bloom: A Review

Horticulturae ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 82
Author(s):  
Amandeep Kaur ◽  
Louise Ferguson ◽  
Niels Maness ◽  
Becky Carroll ◽  
William Reid ◽  
...  
Keyword(s):  
Tree Height ◽  
The United States ◽  
Reproductive Organs ◽  
Tree Age ◽  
The Us ◽  
Nut Production ◽  
Freeze Damage ◽  
Terminal Buds ◽  
And Function ◽  
The Impact

Pecan is native to the United States. The US is the world’s largest pecan producer with an average yearly production of 250 to 300 million pounds; 80 percent of the world’s supply. Georgia, New Mexico, Texas, Arizona, Oklahoma, California, Louisiana, and Florida are the major US pecan producing states. Pecan trees frequently suffer from spring freeze at bud break and bloom as the buds are quite sensitive to freeze damage. This leads to poor flower and nut production. This review focuses on the impact of spring freeze during bud differentiation and flower development. Spring freeze kills the primary terminal buds, the pecan tree has a second chance for growth and flowering through secondary buds. Unfortunately, secondary buds have less bloom potential than primary buds and nut yield is reduced. Spring freeze damage depends on severity of the freeze, bud growth stage, cultivar type and tree age, tree height and tree vigor. This review discusses the impact of temperature on structure and function of male and female reproductive organs. It also summarizes carbohydrate relations as another factor that may play an important role in spring growth and transition of primary and secondary buds to flowers.

scholarly journals Fully-automated root image analysis (faRIA)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Narendra Narisetti ◽  
Michael Henke ◽  
Christiane Seiler ◽  
Astrid Junker ◽  
Jörn Ostermann ◽  
...  
Keyword(s):  
Near Infrared ◽  
Climatic Factors ◽  
Plant Root ◽  
Ground Truth ◽  
Parameter Tuning ◽  
Dice Coefficient ◽  
Powerful Analytical Tool ◽  
Manual Interaction ◽  
And Function ◽  
Noisy Background

AbstractHigh-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

Segmentation of design protocol using EEG

2018 ◽  
Vol 33 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Philon Nguyen ◽  
Thanh An Nguyen ◽  
Yong Zeng
Keyword(s):  
Automated Segmentation ◽  
Manual Segmentation ◽  
Engineering Systems ◽  
Verbal Protocols ◽  
Segmentation Method ◽  
Average Deviation ◽  
Domain Experts ◽  
Design Activities ◽  
The Mind ◽  
Design Protocol

AbstractDesign protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.

scholarly journals A comparative analysis of automated MRI brain segmentation in a large longitudinal dataset: Freesurfer vs. FSL

2020 ◽  
Author(s):  
Javier Quilis-Sancho ◽  
Miguel A. Fernandez-Blazquez ◽  
J Gomez-Ramirez
Keyword(s):  
Comparative Analysis ◽  
Brain Structures ◽  
Automated Segmentation ◽  
Manual Segmentation ◽  
Brain Segmentation ◽  
Clinical Tool ◽  
Brain Volumetry ◽  
Subcortical Brain Structures ◽  
Volume Estimates ◽  
Longitudinal Dataset

AbstractThe study of brain volumetry and morphology of the different brain structures can determine the diagnosis of an existing disease, quantify its prognosis or even help to identify an early detection of dementia. Manual segmentation is an extremely time consuming task and automated methods are thus, gaining importance as clinical tool for diagnosis. In the last few years, AI-based segmentation has delivered, in some cases, superior results than manual segmentation, in both time and accuracy. In this study we aim at performing a comparative analysis of automated brain segmentation. In order to test the performance of automated segmentation methods, the two most commonly used software libraries for brain segmentation Freesurfer and FSL, were put to work in each of the 4028 MRIs available in the study. We find a lack of linear correlation between the segmentation results obtained from Freesurfer and FSL. On the other hand. Freesurfer volume estimates of subcortical brain structures tends to be larger than FSL estimates of same areas. The study builds on an uniquely large, longitudinal dataset of over 4,000 MRIs, all performed with identical equipment to help researchers understand what to expect from fully automated segmentation procedures.

Effects of Lumbar Stabilization Exercises with Real-Time Ultrasound Imaging Biofeedback on Lumbar Multifidus Muscle Cross-Sectional Area in Individuals with Chronic Nonspecific Low Back Pain: A Study Protocol for a Randomized Controlled Trial

2021 ◽  
Author(s):  
Sarafadeen Raheem ◽  
Sokunbi O. Ganiyu ◽  
Aminu A. Ibrahim ◽  
Anas Ismail ◽  
Mukadas O. Akindele ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Cross Sectional Area ◽  
Low Back ◽  
Sectional Area ◽  
Multifidus Muscle ◽  
Cross Sectional ◽  
Lumbar Multifidus ◽  
Lumbar Stabilization ◽  
Lumbar Multifidus Muscle

Abstract Background: Impairments in the lumbar multifidus muscle such as reduced muscle thickness and fat infiltrations are evident in individuals with low back pain. Lumbar stabilization exercises (LSE) with real-time ultrasound imaging (RUSI) biofeedback has been reported to improve preferential activation of as well as retention in the ability to activate of the lumbar multifidus muscle, thus enhancing recovery. However, the effects of using this treatment approach in individuals with nonspecific chronic low back pain (NCLBP) seemed not to have widely reported. The purpose of this study is, therefore, to investigate the effects of LSE with RUSI biofeedback on lumbar multifidus muscle cross-sectional area in individuals with NCLBP patients. Method: This study is a prospective, single-center, assessor-blind three-arm, randomized controlled to be conducted at National Orthopedic Hospital, Kano State, Nigeria. Ninety-one individuals with NCLBP will be randomly assigned into one of the three treatment groups of equal sample size (n = 30); LSE group, LSE with RUSI biofeedback group, or control (minimal intervention). The participants in the LSE and LSE with RUSI biofeedback group will also receive the same intervention as the control group. All participants will receive treatment twice weekly for 8 weeks. The primary outcome will be lumbar multifidus muscles cross-sectional area while the secondary outcomes will be pain, functional disability and quality of life. All outcomes will be assessed at baseline, and at 8 weeks and 3 months post-intervention.Discussion: The outcome of the study may support the evidence for the effectiveness of LSE with RUSI biofeedback in the rehabilitation of individuals with NCLBP. It may also provide a rationale for the physiotherapists to make use of diagnostic ultrasound as a feedback mechanism in enhancing the performance and retention of LSE program as well as monitoring the patient’s recovery.Trial registration: Pan African Clinical Trials Registry, (PACTR201801002980602), Registered on 16 January 2018.

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