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Author |
Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva |
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Title |
Assessment of intracoronary stent location and extension in intravascular ultrasound sequences |
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Journal Article |
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Year |
2019 |
Publication |
Medical Physics |
Abbreviated Journal |
MEDPHYS |
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Volume |
46 |
Issue |
2 |
Pages |
484-493 |
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Keywords  |
IVUS; malapposition; stent; ultrasound |
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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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MILAB; no proj |
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no |
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Admin @ si @ BCR2019 |
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3231 |
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Author |
Lluis Garrido; M.Guerrieri; Laura Igual |
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Title |
Image Segmentation with Cage Active Contours |
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Journal Article |
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Year |
2015 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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Volume |
24 |
Issue |
12 |
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5557 - 5566 |
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Keywords |
Level sets; Mean value coordinates; Parametrized active contours; level sets; mean value coordinates |
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Abstract |
In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods. |
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1057-7149 |
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MILAB |
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no |
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Admin @ si @ GGI2015 |
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2673 |
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Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Title |
On the completeness of feature-driven maximally stable extremal regions |
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2016 |
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Pattern Recognition Letters |
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PRL |
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74 |
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9-16 |
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Local features; Completeness; Maximally Stable Extremal Regions |
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By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered. |
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Elsevier B.V. |
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0167-8655 |
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LAMP;MILAB; |
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no |
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Admin @ si @ MCG2016 |
Serial |
2748 |
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Author |
Frederic Sampedro; Sergio Escalera; Anna Puig |
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Title |
Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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46 |
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1-10 |
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Keywords |
Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation |
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Abstract |
In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios. |
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HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ SEP2014 |
Serial |
2550 |
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Author |
Francesco Ciompi; Oriol Pujol; Carlo Gatta; Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title |
HoliMab: A Holistic Approach for Media-Adventitia Border Detection in Intravascular Ultrasound |
Type |
Journal Article |
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Year |
2012 |
Publication |
Medical Image Analysis |
Abbreviated Journal |
MIA |
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Volume |
16 |
Issue |
6 |
Pages |
1085-1100 |
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Keywords |
Media–Adventitia border detection; Intravascular ultrasound; Multi-Scale Stacked Sequential Learning; Error-correcting output codes; Holistic segmentation |
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Abstract |
We present a fully automatic methodology for the detection of the Media-Adventitia border (MAb) in human coronary artery in Intravascular Ultrasound (IVUS) images. A robust border detection is achieved by means of a holistic interpretation of the detection problem where the target object, i.e. the media layer, is considered as part of the whole vessel in the image and all the relationships between tissues are learnt. A fairly general framework exploiting multi-class tissue characterization as well as contextual information on the morphology and the appearance of the tissues is presented. The methodology is (i) validated through an exhaustive comparison with both Inter-observer variability on two challenging databases and (ii) compared with state-of-the-art methods for the detection of the MAb in IVUS. The obtained averaged values for the mean radial distance and the percentage of area difference are 0.211 mm and 10.1%, respectively. The applicability of the proposed methodology to clinical practice is also discussed. |
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MILAB;HuPBA |
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Admin @ si @ CPG2012 |
Serial |
1995 |
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