Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches

Bruse, Jan L.; Zuluaga, María A.; Khushnood, Abbas; McLeod, Kristin; Ntsinjana, Hopewell; Hsia, Tain‐Yen; Sermesant, Maxime; Pennec, Xavier; Taylor, Andrew M.; Schievano, Silvia

doi:10.1109/tbme.2017.2655364

Cited by 74 publications

(35 citation statements)

References 46 publications

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“…For gene expression data sets under samples, the hierarchical clustering were used to display their sample neighbors usually [27], but the method was likely to cause loose sample neighbors. By D-plots [19], t-SNE-SS(m) maps were able to generate more valid gene neighbors compared to t-SNE-SSP, where m was the dimension of samples.…”

Section: Resultsmentioning

confidence: 99%

Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps

2018

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BackgroundFor analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. However, it is difficult to integrate clustering and visualizing techniques when the similarities of samples and genes are defined by PCC(Person correlation coefficient) measure.ResultsHere, for rare samples of gene expression data sets, we use MG-PCC (mini-groups that are defined by PCC) algorithm to divide them into mini-groups, and use t-SNE-SSP maps to display these mini-groups, where the idea of MG-PCC algorithm is that the nearest neighbors should be in the same mini-groups, t-SNE-SSP map is selected from a series of t-SNE(t-statistic Stochastic Neighbor Embedding) maps of standardized samples, and these t-SNE maps have different perplexity parameter. Moreover, for PCC clusters of mass genes, they are displayed by t-SNE-SGI map, where t-SNE-SGI map is selected from a series of t-SNE maps of standardized genes, and these t-SNE maps have different initialization dimensions. Here, t-SNE-SSP and t-SNE-SGI maps are selected by A-value, where A-value is modeled from areas of clustering projections, and t-SNE-SSP and t-SNE-SGI maps are such t-SNE map that has the smallest A-value.ConclusionsFrom the analysis of cancer gene expression data sets, we demonstrate that MG-PCC algorithm is able to put tumor and normal samples into their respective mini-groups, and t-SNE-SSP(or t-SNE-SGI) maps are able to display the relationships between mini-groups(or PCC clusters) clearly. Furthermore, t-SNE-SS(m)(or t-SNE-SG(n)) maps are able to construct independent tree diagrams of the nearest sample(or gene) neighbors, where each tree diagram is corresponding to a mini-group of samples(or genes).Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2495-5) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps

2018

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“…Bruse et al ( 25 ) focused on data analysis of image processing that will assist clinicians in decision making during MDD;…”

Section: Methodsmentioning

confidence: 99%

Medical Device Development Process, and Associated Risks and Legislative Aspects-Systematic Review

Klímová

Honegr

Kuča

et al. 2020

Front. Public Health

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Objective: Medical device development, from the product's conception to release to market, is very complex and relies significantly on the application of exact processes. This paper aims to provide an analysis and summary of current research in the field of medical device development methodologies, discuss its phases, and evaluate the associated legislative and risk aspects. Methods: The literature search was conducted to detect peer-reviewed studies in Scopus, Web of Science, and Science Direct, on content published between 2007 and November 2019. Based on exclusion and inclusion criteria, 13 papers were included in the first session and 11 were included in the second session. Thus, a total of 24 papers were analyzed. Most of the publications originated in the United States (7 out of 24). Results: The medical device development process comprises one to seven stages. Six studies also contain a model of the medical device development process for all stages or for just some of the stages. These studies specifically describe the concept stage during which all uncertainties, such as the clinical need definition, customer requirements/needs, finances, reimbursement strategy, team selection, and legal aspects, must be considered. Conclusion: The crucial factor in healthcare safety is the stability of factors over a long production time. Good manufacturing practices cannot be tested on individual batches of products; they must be inherently built into the manufacturing process. The key issues that must be addressed in the future are the consistency in the classification of devices throughout the EU and globally, and the transparency of approval processes.

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“…Though hierarchical clustering has been applied in solving the hierarchical modular design problem for product components [34], there are still opportunities for further work. To reduce the tedious effort of the programming for hierarchical programming, the 'pdist' and 'linkage' [35] functions in MATLAB software can be utilized to form hierarchical matrix, and the 'dendrogram' function is followed to form a visualized hierarchical tree for DSM elements. How to select the appropriate parameters in acquiring the distance values between elements with 'pdist' and in using the 'linkage' hierarchical clustering method, and which is the most suitable modularity assessment index for evaluating the modular partition for a hierarchical tree are still needed to be explored.…”

Section: Related Workmentioning

confidence: 99%

“…A series of merge operations are then followed out that finally lead all objects to the same group. A framework and formal pseudocode for agglomerative hierarchical clustering can be referred in [35] and Xu and Wunsch [37].…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

Determining optimal granularity level of modular product with hierarchical clustering and modularity assessment

Wang

Yin

2019

J Braz. Soc. Mech. Sci. Eng.

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Modular product architecture is beneficial for the product maintenance, upgrade, components' concurrent design through team work, and achieving the mass customization eventually. Recent researches tend to group elements into modules as a flat map, but this is inconsistent with the nested composition in product final assembly. Finding the hierarchical modular partition for elements of a product and obtaining its optimal granularity level are still necessary to ease the partition and combination for sub-design tasks. We use the design structure matrix (DSM) to represent the relationships among elements of a product. The hierarchical clustering functions such as pdist and linkage within MATLAB software are utilized to form the hierarchical dendrogram for DSM elements, and the modularity index Q is applied to assess the modular clustering results. The partitions are acquired by various distance threshold values in the vertical axis of the hierarchical dendrogram. The 'cityblock' and 'average' are found as preferred parameters for pdist and linkage functions, respectively, from loop test experiments. Combining the modularity formula in a research literature, a hierarchical modular architecture design methodology is proposed in this work to determine the optimal granularity level of a modular product and to provide solutions for designer selection. Case studies and comparisons in a benchmark problem and a concrete spraying machine illustrate the effectiveness and efficiency of the proposed method. It is characterized by being able to find stable modular partition results for both float and binary DSM elements and satisfying the recommended modular number rule simultaneously.

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Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches

Abstract: Detecting disease-specific clusters within medical image data could improve image-based risk assessment, treatment planning, and medical device development in complex disease.

Cited by 74 publications

References 46 publications

Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps

Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps

Medical Device Development Process, and Associated Risks and Legislative Aspects-Systematic Review

Determining optimal granularity level of modular product with hierarchical clustering and modularity assessment

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