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Author Simone Balocco; Carlo Gatta; Marina Alberti; Xavier Carrillo; Juan Rigla; Petia Radeva edit   pdf
doi  openurl
  Title Relation between plaque type, plaque thickness, blood shear stress and plaque stress in coronary arteries assessed by X-ray Angiography and Intravascular Ultrasound Type Journal Article
  Year 2012 Publication (up) Medical Physics Abbreviated Journal MEDPHYS  
  Volume 39 Issue 12 Pages 7430-7445  
  Keywords  
  Abstract PMID 23231293
PURPOSE:
Atheromatic plaque progression is affected, among others phenomena, by biomechanical, biochemical, and physiological factors. In this paper, the authors introduce a novel framework able to provide both morphological (vessel radius, plaque thickness, and type) and biomechanical (wall shear stress and Von Mises stress) indices of coronary arteries.
METHODS:
First, the approach reconstructs the three-dimensional morphology of the vessel from intravascular ultrasound (IVUS) and Angiographic sequences, requiring minimal user interaction. Then, a computational pipeline allows to automatically assess fluid-dynamic and mechanical indices. Ten coronary arteries are analyzed illustrating the capabilities of the tool and confirming previous technical and clinical observations.
RESULTS:
The relations between the arterial indices obtained by IVUS measurement and simulations have been quantitatively analyzed along the whole surface of the artery, extending the analysis of the coronary arteries shown in previous state of the art studies. Additionally, for the first time in the literature, the framework allows the computation of the membrane stresses using a simplified mechanical model of the arterial wall.
CONCLUSIONS:
Circumferentially (within a given frame), statistical analysis shows an inverse relation between the wall shear stress and the plaque thickness. At the global level (comparing a frame within the entire vessel), it is observed that heavy plaque accumulations are in general calcified and are located in the areas of the vessel having high wall shear stress. Finally, in their experiments the inverse proportionality between fluid and structural stresses is observed.
 
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  Notes MILAB Approved no  
  Call Number Admin @ si @BGA2012 Serial 2170  
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Author Francesco Ciompi; Simone Balocco; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva edit  doi
openurl 
  Title Computer-Aided Detection of Intra-Coronary Stent in Intravascular Ultrasound Sequences Type Journal Article
  Year 2016 Publication (up) Medical Physics Abbreviated Journal MP  
  Volume 43 Issue 10 Pages  
  Keywords  
  Abstract Purpose: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI), in order to prevent acute vessel occlusion. The identication of struts location and the denition of the stent shape are relevant for PCI planning 15 and for patient follow-up. We present a fully-automatic framework for Computer-Aided Detection
(CAD) of intra-coronary stents in Intravascular Ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape.

Methods: The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classication. The output of the classication 20 stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multi-centric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bio-absorbable stents.

Results: The method was able to detect structs in both metallic stents with an overall F-measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bio-absorbable 25 stents with an overall F-measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts.

Conclusions: The results are close to the inter-observer variability and suggest that the system has the potential of being used as method for aiding percutaneous interventions.
 
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  Notes MILAB Approved no  
  Call Number Admin @ si @ CBR2016 Serial 2819  
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Author Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva edit  url
doi  openurl
  Title Assessment of intracoronary stent location and extension in intravascular ultrasound sequences Type Journal Article
  Year 2019 Publication (up) Medical Physics Abbreviated Journal MEDPHYS  
  Volume 46 Issue 2 Pages 484-493  
  Keywords IVUS; malapposition; stent; ultrasound  
  Abstract PURPOSE:

An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter-based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment.
METHODS:

The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm.
RESULTS:

The technique was extensively validated on a set of 103 IVUS of sequences of in vivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F-measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5 mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F-measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5 mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present.
CONCLUSIONS:

Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method.
 
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  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ BCR2019 Serial 3231  
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Author I. Sorodoc; S. Pezzelle; A. Herbelot; Mariella Dimiccoli; R. Bernardi edit  url
doi  openurl
  Title Learning quantification from images: A structured neural architecture Type Journal Article
  Year 2018 Publication (up) Natural Language Engineering Abbreviated Journal NLE  
  Volume 24 Issue 3 Pages 363-392  
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  Abstract Major advances have recently been made in merging language and vision representations. Most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw multimodal data to perform certain types of higher level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like few, some and all. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in most fish are red, most encodes the proportion of fish which are red fish. In this paper, we study how well current neural network strategies model such relations. We propose a task where, given an image and a query expressed by an object–property pair, the system must return a quantifier expressing which proportions of the queried object have the queried property. Our contributions are twofold. First, we show that the best performance on this task involves coupling state-of-the-art attention mechanisms with a network architecture mirroring the logical structure assigned to quantifiers by classic linguistic formalisation. Second, we introduce a new balanced dataset of image scenarios associated with quantification queries, which we hope will foster further research in this area.  
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  Notes MILAB; no menciona Approved no  
  Call Number Admin @ si @ SPH2018 Serial 3021  
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Author Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla edit  doi
openurl 
  Title Editorial: Special Issue on Computational Intelligence for Vision and Robotics Type Journal Article
  Year 2017 Publication (up) Neural Computing and Applications Abbreviated Journal Neural Computing and Applications  
  Volume 28 Issue 5 Pages 853–854  
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  Notes HuPBA;MILAB; no menciona Approved no  
  Call Number Admin @ si @ GGE2017 Serial 2845  
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