Cascaded Model (Conventional + Deep Learning) for Weakly Supervised Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images

Sharan, Taresh Sarvesh; Bhattacharjee, Romel; Tiwari, Alok; Sharma, Sakshi; Sharma, Neeraj

doi:10.1080/02564602.2022.2055668

Cited by 1 publication

(3 citation statements)

References 24 publications

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“…al [ 13 ] ED 0.9580 0.9098 0.9055 ES Silva et. al [ 32 ] ED 0.9602 0.8902 0.9257 ES 0.9337 0.8871 0.8543 Sharan et.al [ 10 ] ED 0.9210 × × ES × × Yang et al [ 22 ] ED 0.8982 ES Wang et al [ 34 ] ED 0.9620 × × ES 0.9390 × × Budai et al [ 20 ] ED 0.9270 × 0.873 ES × Cheng et al [ 17 ] ED 0.9490 0.888 0.888 ES Abdeltawab et al [ 16 ] ED 0.9600 0.8800 × ES …”

Section: Discussionmentioning

confidence: 99%

“…Various semi-automatic and automatic cardiac segmentation methods have been developed. Early segmentation methods employed semi-automatic segmentation approaches such as those presented in the work of Ding et al [ 9 ], Sharan et al [ 10 ] and Decourt et al [ 11 ]. Semi-automatic methods necessitate significant user intervention, as a result, they are unsuitable for applications requiring rapid segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the biventricular suffers from distorted unclear borderline. To address these shortcomings, the proposed framework in this paper is inspired by ResNet and UNet of the aforementioned methods, which breaks down the segmentation process into two steps: localization and segmentation [ 2 , 10 , 14 , 15 , 17 ]. However, unlike previous methods, each step is designed with specific techniques capable of producing promising results while considering the segmentation time.…”

Section: Introductionmentioning

confidence: 99%

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CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation

Shoieb,

Fathalla,

Youssef

et al. 2023

Phys Eng Sci Med

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Cardiac image segmentation is a critical step in the early detection of cardiovascular disease. The segmentation of the biventricular is a prerequisite for evaluating cardiac function in cardiac magnetic resonance imaging (CMRI). In this paper, a cascaded model CAT-Seg is proposed for segmentation of 3D-CMRI volumes. CAT-Seg addresses the problem of biventricular confusion with other regions and localized the region of interest (ROI) to reduce the scope of processing. A modified DeepLabv3+ variant integrating SqueezeNet (SqueezeDeepLabv3+) is proposed as a part of CAT-Seg. SqueezeDeepLabv3+ handles the different shapes of the biventricular through the different cardiac phases, as the biventricular only accounts for small portion of the volume slices. Also, CAT-Seg presents a segmentation approach that integrates attention mechanisms into 3D Residual UNet architecture (3D-ResUNet) called 3D-ARU to improve the segmentation results of the three major structures (left ventricle (LV), Myocardium (Myo), and right ventricle (RV)). The integration of the spatial attention mechanism into ResUNet handles the fuzzy edges of the three structures. The proposed model achieves promising results in training and testing with the Automatic Cardiac Diagnosis Challenge (ACDC 2017) dataset and the external validation using MyoPs. CAT-Seg demonstrates competitive performance with state-of-the-art models. On ACDC 2017, CAT-Seg is able to segment LV, Myo, and RV with an average minimum dice symmetry coefficient (DSC) performance gap of 1.165%, 4.36%, and 3.115% respectively. The average maximum improvement in terms of DSC in segmenting LV, Myo and RV is 4.395%, 6.84% and 7.315% respectively. On MyoPs external validation, CAT-Seg outperformed the state-of-the-art in segmenting LV, Myo, and RV with an average minimum performance gap of 6.13%, 5.44%, and 2.912% respectively.

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