A joint multi-scale convolutional network for fully automatic segmentation of the left ventricle

Tong, Qianqian; Yuan, Zhiyong; Liao, Xiangyun; Zheng, Mianlun; Zhu, Weixu; Zhang, Guian; Ning, Munan

doi:10.1109/icip.2017.8296855

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…In [47], the region that has the greatest intersection with the segmented region of the previous slice is chosen. Mathematical morphology operations can be used to ill holes [45,101].…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…This practice is common in DL methods, in which networks usually need images with a speciic size. In some cases [23,101] automatic ROI extraction procedures were presented. Studies that do not deine automatic methods or report only that there have been changes in the images are classiied as semi-automatic.…”

Section: Segmentation Processmentioning

confidence: 99%

“…The initial contour is used in the initialization of a more precise method, such as a DM [119] or a more speciic AI model [103]. The sub-image is primarily obtained based on regression, in which the coordinates of the delimiting rectangle are estimated [5,38,72,90,101,126,127]. Regression is also used to obtain the center of the LV [98].…”

Section: Automatic Roi Extractionmentioning

confidence: 99%

“…On the other hand, in [103], the Omega-Net variation has a speciic module that performs image transformations, preparing them to be used as input to the U-Net network. Images of diferent resolutions can be used to improve accuracy in edge regions [101,131].…”

Section: Artificial Intelligencementioning

confidence: 99%

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Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade

Ribeiro

Nunes

2022

ACM Comput. Surv.

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Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to diagnose heart disease. However, the repetitive manual segmentation of these images requires considerable human effort and can decrease diagnostic accuracy. In recent years, several fully and semi-automatic approaches have been proposed, mainly using image-based, atlas, graphs, deformable models, and artificial intelligence methods. This paper presents a systematic mapping on the left ventricle segmentation, considering 74 studies published in the last decade. The main contributions of this review are: definition of the main segmentation challenges in these images; proposal of a new schematization, dividing the segmentation process into stages; categorization and analysis of the segmentation methods, including hybrid combinations; and analysis of the evaluation process, metrics, and databases. The performance of the methods in the most used public database is assessed, and the main limitations, weaknesses, and strengths of each method category are presented. Finally, trends, challenges, and research opportunities are discussed. The analysis indicates that methods from all categories can achieve good performance, and hybrid methods combining deep learning and deformable models obtain the best results. Methods still fail in specific slices, segment wrong regions, and produce anatomically impossible segmentations.

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“…In [47], the region that has the greatest intersection with the segmented region of the previous slice is chosen. Mathematical morphology operations can be used to ill holes [45,101].…”

Section: Hybrid Methodsmentioning

confidence: 99%

Section: Segmentation Processmentioning

confidence: 99%