Clinical-Guided Strategy Towards a Spatio-Temporal Cardiac MRI Right Ventricular Short-Axis (ST-CMRI-RVSA) Labeled Dataset

Ammari, Asma; Saouli, Rachida; Hmida, Badii; Maatouk, M.; Bedoui, Mohamed Hédi

doi:10.1007/s42979-022-01144-7

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…It is important to note that these two coefficients do not exceed 5% and 8% when analyzing the LV [61]. In fact, inter-expert variability has a major influence in interpreting the segmentation quality results as shown in Ammari et al work's [62]. For this reason, we wanted to compare our algorithm's output with two different manual segmentation held by two expert radiologists from Fattouma Bourguiba hospital.…”

Section: Myocardium Extractionmentioning

confidence: 99%

“…Before taking part in this study, all volunteers gave their informed consent for inclusion. Images series can be downloaded from the ST-CMRI-RVSA dataset 62 with is online available and for research purposes only.…”

Section: Recursive Filter Buildingmentioning

confidence: 99%

“…61 In fact, inter-expert variability has a major influence in interpreting the segmentation quality results as shown in Ammari et al work's. 62 For this reason, we First and second columns: expert 1 and expert 2 annotations of different slices of patient 1 respectively. Fourth and fifth columns: expert 1 and expert 2 annotations of different slices of patient 2 respectively.…”

Section: Myocardium Extractionmentioning

confidence: 99%

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Ventricular segmentation and modeling using topological watershed transformation and harmonic state model

Mahmoudi

2022

Int J Imaging Syst Tech

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This paper proposes an adapted ventricular segmentation method based on topological watershed transform. Segmentation will allow spatio-temporal modeling of trajectories of the different points belonging to the borders of the ventricle using a harmonic motion model that is able to describe such motion over the entire cardiac cycle. In addition, extraction of the adopted canonical state vector and the corresponding state equations guarantees an optimal efficacy and a gradual transition from order n to order n+1. To validate the proposed approach, an intern-image base was used. Our results show a promising ability to discern whether subjects are healthy or pathological with an 80% success rate.

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Section: Myocardium Extractionmentioning

confidence: 99%

Section: Recursive Filter Buildingmentioning

confidence: 99%

Section: Myocardium Extractionmentioning

confidence: 99%

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Ventricular segmentation and modeling using topological watershed transformation and harmonic state model

Mahmoudi

2022

Int J Imaging Syst Tech

Self Cite

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“…A. Khalil et al [17] create a model that uses image synthesis as well as multi-fusion segmentation to segment all tri-cardiac components. Ammari, A et al [18] developed a customizable method for extracting and annotating informative slices, beginning with complete CMRI sequences. H. El-Rewaidy et al [19] present a work for modelling the RV surface utilizing various 2Dcontours, including information from various trans-sectional scans into the single set.…”

Section: Literature Surveymentioning

confidence: 99%

Precise Identification and Quantitative Analysis of Right Ventricle Using Hierarchical Intensity Clustering with Markov Random Field for Cardiac Mr Images

Yadav¹

2023

cienc.eng.

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Precise localization and quantitative analysis of the right ventricle from cardiac magnetic resonance imaging (CMRI) images is imperative for assessing cardiopulmonary and cardiovascular mal-functionalities. Due to its poorly defined borders, precise contouring of the RV in CMRI images continues to be difficult. An approach for contouring the right ventricle based on a hierarchical intensity-based clustering method followed by Markov random field is proposed in this paper to overcome this difficulty.Because our method offers localization for each time step, it enables comprehensive right ventricle analysis during the whole cardiac-cycle. It also allows automatic prediction systems of volumetric parameters such as end systole and end diastolestages.48 human participants' cardiac MRI scans were used to validate the method. The presented scheme yielded significantly less variance than (approximately one half) compared to the reference standard of manually determined RV contours by clinical experts. This approach obtained mean Dice-Coefficient and Haussdorff-Distance of 0.92 and 5.25 mm 0.94 and 5.68 mm on the validation and tests. Further the results are evaluated using diagnosis metrics such as end diastole volume, end systole volume, and ejection fraction. Our model heading towards accurate RV localization at endocardium borders in cardiac MRI.

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Deep-active-learning approach towards accurate right ventricular segmentation using a two-level uncertainty estimation

Ammari

Saouli

Hmida

et al. 2023

Computerized Medical Imaging and Graphics

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Clinical-Guided Strategy Towards a Spatio-Temporal Cardiac MRI Right Ventricular Short-Axis (ST-CMRI-RVSA) Labeled Dataset

Cited by 3 publications

References 15 publications

Ventricular segmentation and modeling using topological watershed transformation and harmonic state model

Ventricular segmentation and modeling using topological watershed transformation and harmonic state model

Precise Identification and Quantitative Analysis of Right Ventricle Using Hierarchical Intensity Clustering with Markov Random Field for Cardiac Mr Images

Deep-active-learning approach towards accurate right ventricular segmentation using a two-level uncertainty estimation

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