Segmentation of medical images using mean value guided contour

Kiaei, Ali Akbar; Khotanlou, Hassan

doi:10.1016/j.media.2017.06.005

Cited by 23 publications

(14 citation statements)

References 43 publications

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“…The study results show that native machine learning prescribes drugs that cover a wider range of human genes involved in MonkeyPox. Different studies use machine learning in medicine (Kiaei et al, 2019;Kiaei and Khotanlou, 2017). The validity of the proposed drug combinations has been investigated by a systematic review, gene network metaanalysis, and prescription drug information.…”

Section: Discussionmentioning

confidence: 99%

Drug combinations proposed by machine learning on genes/proteins to improve the efficacy of Tecovirimat in the treatment of Monkeypox: A Systematic Review and Network Meta-analysis

Boush

Kiaei

Safaei

et al. 2023

Preprint

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Background: The World Health Organization (WHO) describes Monkeypox as a viral zoonosis, or an animal-to-human virus transmission, with symptoms comparable to those of past smallpox patients but clinically less severe. This study's objective is to assess the results of previous investigations on the best drug combinations for treating Monkeypox. Method: The pharmacological combinations used to treat monkeypox sickness have been researched in two stages for this systematic review and network meta-analysis. To begin with, a certain machine learning technique is used to extract the medication combinations from the researched articles offered on science databases, including Scopus, PubMed, Web of Science (ISI), Science Direct, Embase, and Google Scholar. Second, the tested medicine combinations will have been proven. Results: The results of this study show that the p-value between the proposed drug combination and Monkeypox for scenarios 1 to 5 were 0.108, 0.042, 0.023, 0.018, and 0.015, respectively. Scenario i is the combination of the first i suggested drugs for treating Monkeypox. This has led to a 720 percent increase in the proposed drug combination's efficacy in treating Monkeypox. Conclusion: The suggested drug combination decreases the p-value between MonkeyPox and the genes as potential targets for Monkeypox progression, which leads to an improvement in the treatment of Monkeypox. Therefore, using the right combination of drugs is important in improving the community's health and reducing per capita treatment costs. Keywords: Genes, Biological data, Drug Combination, Systematic Review, Network Meta-analysis, Machine Learning, MonkeyPox.

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Section: Discussionmentioning

confidence: 99%

Drug combinations proposed by machine learning on genes/proteins to improve the efficacy of Tecovirimat in the treatment of Monkeypox: A Systematic Review and Network Meta-analysis

Boush

Kiaei

Safaei

et al. 2023

Preprint

Self Cite

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“…The assumption of this approach is that structures of interest have a repetitive form of geometry. It involves expert manual segmentation of the structures of interest, and registration of the training examples to a common pose or model training to build the model [ 20 ] [ 21 ] [ 22 ]. This type involves significant amount of expert time.…”

Section: Discussionmentioning

confidence: 99%

Novel Wavelet-Based Segmentation of Prostate CBCT Images with Implanted Calypso Transponders

Liu¹,

Saleh

Song

et al. 2017

IJMPCERO

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Segmentation of prostate Cone Beam CT (CBCT) images is an essential step towards real-time adaptive radiotherapy (ART). It is challenging for Calypso patients, as more artifacts generated by the beacon transponders are present on the images. We herein propose a novel wavelet-based segmentation algorithm for rectum, bladder, and prostate of CBCT images with implanted Calypso transponders. For a given CBCT, a Moving Window-Based Double Haar (MWDH) transformation is applied first to obtain the wavelet coefficients. Based on a user defined point in the object of interest, a cluster algorithm based adaptive thresholding is applied to the low frequency components of the wavelet coefficients, and a Lee filter theory based adaptive thresholding is applied on the high frequency components. For the next step, the wavelet reconstruction is applied to the thresholded wavelet coefficients. A binary (segmented) image of the object of interest is therefore obtained. 5 hypofractionated Calypso prostate patients with daily CBCT were studied. DICE, Sensitivity, Inclusiveness and ΔV were used to evaluate the segmentation result.

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“…segmentation [6]- [8], region segmentation [9], [10], contour extraction approaches [11], [12], and atlas-based methods [13], [14]. Shen et al [15] successfully minimize the oversegmentation of neighborhood regions by using a support vector machine (SVM) classifier to combine a two-way chain coding method.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Automatic Lung Segmentation on Chest CT Scans

Peng

Wang

et al. 2020

IEEE Access

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Accurate lung segmentation in chest Computed Tomography (CT) scans is a challenging problem because of variations in lung volume shape, susceptibility to partial volume effects that affect thin antero-posterior junction lines, and lack of contrast between the lung and surrounding tissues. To address the need for a robust method for lung segmentation, we present a new method, called Pixel-based two-Scan Connected Component Labeling-Convex Hull-Closed Principal Curve method (PSCCL-CH-CPC), which automatically detects lung boundaries, and surpasses state-of-the-art performance. The proposed method has two main steps: 1) an image preprocessing step to extract coarse lung contours, and 2) a refinement step to refine the coarse segmentation result on the basis of the improved principal curve model and the machine learning model. Experimental results show that the proposed method has good performance, with a Dice Similarity Coefficient (DSC) as high as 98.21%. When compared with state-of-the-art methods, our proposed method achieved superior segmentation results, with an average DSC of 96.9%. INDEX TERMS Automatic lung segmentation, chest CT scans, principal curve, closed principal curve method, machine learning.

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Segmentation of medical images using mean value guided contour

Cited by 23 publications

References 43 publications

Drug combinations proposed by machine learning on genes/proteins to improve the efficacy of Tecovirimat in the treatment of Monkeypox: A Systematic Review and Network Meta-analysis

Drug combinations proposed by machine learning on genes/proteins to improve the efficacy of Tecovirimat in the treatment of Monkeypox: A Systematic Review and Network Meta-analysis

Novel Wavelet-Based Segmentation of Prostate CBCT Images with Implanted Calypso Transponders

Hybrid Automatic Lung Segmentation on Chest CT Scans

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