Automatic Left Ventricle Quantification in Cardiac MRI via Hierarchical Refinement of High-Level Features by a Salient Perceptual Grouping Model
| Autor: | Angélica Atehortúa, Mireille Garreau, David Romo-Bucheli, Eduardo Romero |
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| Přispěvatelé: | Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universidad Nacional de Colombia [Bogotà] (UNAL), Région Bretagne 647 (2015Labex ANR-11-LABX-0004, Li S.McLeod K.Young A.Rhode K.Pop M.Zhao J.Mansi T.Sermesant M. (eds), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-11-LABX-0004,CAMI,Gestes Médico-Chirurgicaux Assistés par Ordinateur(2011) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Word error rate
Intensity profiles 030204 cardiovascular system & hematology 030218 nuclear medicine & medical imaging LV cardiac indicators Set (abstract data type) 03 medical and health sciences symbols.namesake 0302 clinical medicine Cardiac magnetic resonance imaging medicine Segmentation Cardiac MRI Mathematics Ejection fraction Saliency Pixel medicine.diagnostic_test business.industry Pattern recognition Deep learning Pearson product-moment correlation coefficient medicine.anatomical_structure Ventricle symbols [SDV.IB]Life Sciences [q-bio]/Bioengineering Artificial intelligence business |
| Zdroj: | 9th International Workshop on Statistical Atlases and Computational Models of the Heart Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 9th International Workshop on Statistical Atlases and Computational Models of the Heart Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Sep 2018, Granada, Spain. pp.439-449, ⟨10.1007/978-3-030-12029-0_47⟩ Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges ISBN: 9783030120283 STACOM@MICCAI |
| DOI: | 10.1007/978-3-030-12029-0_47⟩ |
| Popis: | International audience; An accurate segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) provides reliable cardiac indexes such as the ventricular volume, the ejection fraction or regional wall thicknesses (RWT). This paper introduces an automated method to compute such indexes in 2D MRI slices from a semantic segmentation obtained in two steps. A first coarse segmentation is obtained by applying an encoder-decoder neural network architecture that assigns a probability value to each pixel. Afterwards, this segmentation is refined by a spatio-temporal saliency analysis. The method was evaluated in MR sequences of 175 subjects divided in two groups training (145 subjects) and test (30 subjects). For the training data set, using a K-cross validation setup, the method achieves an average Pearson correlation coefficient of 0.98, 0.92, 0.95 and 0.75 with the set of indexes LV cavity, myocardium areas, cavity dimensions and region wall thicknesses, respectively, while classification of the cardiac phase yielded a rate of $$10.01\%$$. For the same set of indexes, evaluated in the test dataset, an average Pearson correlation coefficient of 0.98, 0.87, 0.97 and 0.66 was obtained. Additionally, the cardiac phase classification error rate was $$9\%$$. The method provides a reliable LV segmentation and quantification of cardiac indexes. © 2019, Springer Nature Switzerland AG. |
| Databáze: | OpenAIRE |
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