Left Ventricle Segmentation Using a Combination of Region Growing and Graph Based Method

Oghli, Mostafa Ghelich; Mohammadzadeh, Maryam; Mohammadzadeh, Vahid; Kadivar, Sakineh; Zadeh, Ali Mohammad

doi:10.5812/iranjradiol.42272

Cited by 6 publications

(5 citation statements)

References 28 publications

(32 reference statements)

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“…Another important point to focus on is the time required to generate this kind of model. Segmentation is a very time-consuming and tedious activity, subject to intra-and inter-observer variability, and requires dedicated expert operators [10], especially in the case of complex anatomies. To give an idea, the complete process to obtain the hollow heart from the raw images to the final smoothed model, without considering the printing phase, took us some tens of hours.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Manual segmentation is still the most diffused approach, even if semi-automatic and automatic tools have gradually been implemented [9]. Manual segmentation is a very timeconsuming and tedious activity, subject to intra-and inter-observer variability, and requires dedicated expert operators [10]. Therefore, the implementation of automated segmentation approaches that could be accurate, robust and requiring as little user interaction as possible is perceived as a fundamental development in the field [9].…”

Section: State Of the Art 21 Reconstruction Of An Anatomical District: Methods And Toolsmentioning

confidence: 99%

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Operative Workflow from CT to 3D Printing of the Heart: Opportunities and Challenges

2021

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Medical images do not provide a natural visualization of 3D anatomical structures, while 3D digital models are able to solve this problem. Interesting applications based on these models can be found in the cardiovascular field. The generation of a good-quality anatomical model of the heart is one of the most complex tasks in this context. Its 3D representation has the potential to provide detailed spatial information concerning the heart’s structure, also offering the opportunity for further investigations if combined with additive manufacturing. When investigated, the adaption of printed models turned out to be beneficial in complex surgical procedure planning, for training, education and medical communication. In this paper, we will illustrate the difficulties that may be encountered in the workflow from a stack of Computed Tomography (CT) to the hand-held printed heart model. An important goal will consist in the realization of a heart model that can take into account real wall thickness variability. Stereolithography printing technology will be exploited with a commercial rigid resin. A flexible material will be tested too, but results will not be so satisfactory. As a preliminary validation of this kind of approach, print accuracy will be evaluated by directly comparing 3D scanner acquisitions to the original Standard Tessellation Language (STL) files.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: State Of the Art 21 Reconstruction Of An Anatomical District: Methods And Toolsmentioning

confidence: 99%

Operative Workflow from CT to 3D Printing of the Heart: Opportunities and Challenges

2021

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“…To our knowledge, there is no other research using the result of a machine-learning classifier as inclusion criteria in a seeded region growing algorithm. There have been approaches to increasing the performance of region growing by combining it with other techniques such as edge detection [ 7 ], graph based methods [ 8 ], advanced thresholding [ 9 , 10 ], increasing the region growing’s adaptiveness [ 11 ] and others. However, none of these methods builds their segmentation decision on a multidimensional input vector.…”

Section: Related Workmentioning

confidence: 99%

Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data

et al. 2023

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Purpose The acquisition conditions of medical imaging are often precisely defined, leading to a high homogeneity among different data sets. Nonetheless, outliers or artefacts still appear and need to be reliably detected to ensure a reliable diagnosis. Thus, the algorithms need to handle small sample sizes especially, when working with domain specific imaging modalities. Methods In this work, we suggest a pipeline for the detection and segmentation of light pollution in near-infrared fluorescence optical imaging (NIR-FOI), based on a small sample size. NIR-FOI produces spatio-temporal data with two spatial and one temporal dimension. To calculate a two-dimensional light pollution map for the entire image stack, we combine region growing and k-nearest neighbours (kNN), which classifies pixels into fore- and background by its entire temporal component. Thus, decision-making on reduced data is omitted. Results We achieved a $$F_1$$ F 1 score of 0.99 for classifying a data set as light polluted or pollution-free. Additionally, we reached a total $$F_1$$ F 1 score of 0.90 for detecting regions of interest within the polluted data sets. Finally, an average Dice’s coefficient measuring the segmentation performance over all polluted data sets of 0.80 was accomplished. Conclusions A Dice’s coefficient of 0.80 for the area segmentation does not seem perfect. However, there are two main factors, besides true prediction errors, lowering the score: Segmentation mistakes on small areas lead to a rapid decrease in the score and labelling errors due to complex data. However, in combination with the light-polluted data set and pollution area detection, these results can be considered successful and play a key role in our general goal: Exploiting NIR-FOI for the early detection of arthritis within hand joints.

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“…After the continuous exploration of left ventricle membranes segmentation by many researchers, there are many methods of the left ventricle endocardial and epicardial segmentation in the literature. The main segmentation methods include threshold method [2] , clustering method [3] , region growth method [4] , graph cut method [5] , deformation model method [6][7][8] , dynamic programming method [9] and hybrid method for image segmentation [10][11][12] . Automated segmentation of the endocardium and epicardial of the heart is the research direction in recent years.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of the Ventricle Membranes in Short‐Axis Sequences by Optical Flow Base on DLSRE Model

Li¹,

Wu²,

2021

Chinese J of Electronics

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Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiac papillary muscle and trabecular muscle do inconformity the clinical requirements. To address these issues, this paper proposes an automatic segment algorithm for continuously extracting ventricle membranes boundary, which adopts optical flow field information and sequential images information. The images are cropped by frame difference method, which according to the continuity of adjacent slices of cardiac MRI images. The roughly boundary of epicardium is extracted by the Double level set region evolution (DLSRE) model, which combines image global information, local information and edge information. The ventricle endocardium and epicardial contours are tracked according to the optical flow field information between image sequences. The segmentation results are optimized by Delaunay triangulation algorithm. The experimental results demonstrate that the proposed method can improve the accuracy of segmenting the ventricle endocardium and epicardium contours, and segment the contour of the smooth ventricle membrane edge that meets the clinical definition.

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Left Ventricle Segmentation Using a Combination of Region Growing and Graph Based Method

Cited by 6 publications

References 28 publications

Operative Workflow from CT to 3D Printing of the Heart: Opportunities and Challenges

Operative Workflow from CT to 3D Printing of the Heart: Opportunities and Challenges

Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data

Segmentation of the Ventricle Membranes in Short‐Axis Sequences by Optical Flow Base on DLSRE Model

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