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BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 14. A comparison between the results
<p> A comparison between the results. Each column shows the superposition of the corresponding results: Caselle (yellow curve), Chan & Vese (curve blue) Lankton (red curve), our method (purple line) and the ground truth (Curve Green) for a normal subject, MCI and AD.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 13.The results of the Corpus Calosum segmentation.
<p> The results of the Corpus Calosum segmentation. The six lines present in order: image zoom on the hippocampus area, manually segmented image, the result of the Caselle method , the result of the Chan&Vese method, the result of the Lankton method, the result of our method. Column 1 shows a healthy subject, column 2 MCI (primary stage), and the third column corresponds to an Alzheimer's subject (advanced stage). </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 12. Results of calculating the Hausdorff distances
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 11. Results of calculating the Hausdorff distance
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p> Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) .and the ground truth about a MCI subject following segmentation of the hippocampus.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 10. The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles, Chan & Vese, Lanktom, and our method) and the ground truth about a subject Normal following the segmentation of the hippocampus</p> <p> </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 9. A comparison between the results
<p>In figure 9 we present a comparison between the manual segmentation, Caselle, Chan&vese, Lankton and our method. </p> <p>A comparison between the results. Each column shows the superposition of the corresponding results: Caselle (yellow curve), Chan & Vese (curve blue), Lankton (red curve), our method (purple curve), and the ground truth (Green Curve) for a normal subject, MCI and AD.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 8. The results of the segmentation of the hippocampus.
<p>The results show the hippocampus segmentation using both Caselle, Chan&Vese, Lankton and our method. </p> <p>The results of the segmentation of the hippocampus. The six lines present: image zoom on the hippocampus area, manually segmented image, the result of the Caselle method, the result of the Chan&Vese method, the result of the Lankton method, the result of our method. Column 1 shows a healthy subject, column 2 shows a MCI (primary stage) and the third column corresponds to an Alzheimer's subject (advanced stage)</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 7. Training for classification
<p>AMED measures the average distance while HD measures the maximum distance between the two vectors. The aim of our method is to classify the test subject in three classes (N, MCI or AD), so for each vector element E we look for the four nearest neighbors. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 5. Improvement using variation
<p>In the figures below a constraint of the variation is used in order to show the limits. The contour may include more areas surrounding the hippocampus, which are not homogeneous with the desired area. Through the confidence interval of variation and priori knowledge can overcome these limitations. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 6. Improvement using Surface
<p>In the figures below, a constraint of the surface is used to show the limits. The contour may include the hippocampus and more areas surrounding it, which are homogeneous with the desired area. Through the surface of the confidence interval and priori knowledge can overcome these limitations. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 2. Three Corpus Calosum: Normal, MCI, AD
<p>The three figures above present the Corpus Callosum relating to three topics: Normal Topic by MCI (Mild Cognitive Impairment), Alzheimer’s topic. Secondly, we will present our clustering method to classify the test subject between 3 classes: N (Normal), MCI (Mild Cognitive Impairment), and AD (Alzheimer's disease). </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 1. Three hippocampus: Normal, MCI, AD
<p>In this context is our work: performing a diagnostic computer-aided system for detecting Alzheimer's disease. Like any diagnostic system, our system contains three parts: preprocessing, segmentation and classification. Initially, we will present a new segmentation method to segment the Hippocampus and Corpus Callosum regardless of the patient's condition. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training
<p> A. Segmentation It seeks to establish a model that describes the shape and typical fluctuations. This requires first the preparation of a learning base to reflect the possible variations in shape of the structure. The preparation of the training set Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 3.Proposed Computer Assisted Diagnosis
<p>The figure below presents our proposed Computer Assisted Diagnosis. Our CAD includes 3 steps: Preprocessing, Segmentation and Classification. For the step of preprocessing, we used the NLMS (Non Local Means) to improve the quality of image. For the step of segmentation: we have a learning phase to extract the different shapes and to determine the average shape. Our proposed automatic method is based on the deformable model. For the step of classification, we present a new supervised method to distinguish between Normal, MCI and AD. The figure below presents our proposed system.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training
<p>Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below. </p>
Data sheet for Robotic assisted milling for increased productivity
<p>FRF(tap test result) in EXCEL - FRF analysis - Fixed support vs mobile supportv4.xlsx<br> Form error in EXCEL - Form error.xlsx<br> Force data in EXCEL - Force data with and without support.xlsx<br> Surface roughness in EXCEL - Surface roughness-Alicona.xlsx</p>
The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews (data repository)
<p>These are the data we generated in our evaluation of the graphical user interface.</p> <p>Please see our publication on Wellcome Open Research for information about the evaluations.</p>
Figure 7A-C in New DNA-assisted records of water mites from Sardinia, with the description of a new species (Acari, Hydrachnidia)
Figure 7A-C. Atractides disabatinoi sp. nov., paratype ♀ [CCDB_44310_HO6], ITALY, Sardinia, Comune di Fluminimaggiore, Sorgente Pubusinu, It 2022-1c. A – coxal and genital field; B – palp, medial view; C – I-L-5 and -6. Scale bar = 100 μm.
Figure 8 in New DNA-assisted records of water mites from Sardinia, with the description of a new species (Acari, Hydrachnidia)
Figure 8. Neighbour-Joining tree of the genus Atractides obtained from 64 nucleotide COI sequences. The results of species delimitation by ASAP procedure are indicated by vertical bars.
Figure 6A-E in New DNA-assisted records of water mites from Sardinia, with the description of a new species (Acari, Hydrachnidia)
Figure 6A-E Atractides disabatinoi sp. nov., holotype ♂ [CCDB_44301_HO9], ITALY, Sardinia, Comune di Fluminimaggiore, outflow of Sorgente Pubusinu, It 2022-2a. A – IV-L-4 and -5 (arrow indicate pointed lateral sheet covering the articulation of the next segment); B – genital field; C – I-L-5 and -6; D – palp, lateral view; E – palp, medial view. F A. robustus (Sokolow, 1940), ♂, MONTENEGRO, Morača river: genital field. Scale bars = 100 μm.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.