Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation

Zhen, Xiantong; Wang, Zhijie; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo

doi:10.1016/j.media.2015.07.003

Cited by 106 publications

(69 citation statements)

References 53 publications

(83 reference statements)

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“…In [6], the authors made a detailed comparison of their method with other existing estimation methods. The comparison results showed that the direct estimation methods possessed attractive advantages over the previous segmentation-based estimation methods.…”

Section: Results and Analysismentioning

confidence: 99%

“…As illustrated in Table 2, our experiment results validated this view. Methods LV volume estimation errors Our method 0.014 ± 0.015 Zhen et al [6] 0.010 ± 0.011 Wang et al [5] 0.016 ± 0.019 Ayed et al [3] (level set) 0.036 ± 0.025 Ayed et al [2] (graph cut) 0.029 ± 0.027…”

Section: Results and Analysismentioning

confidence: 99%

“…Unlike segmentation-based methods, segmentation-free methods try to estimate the cardiac ventricular volumes from MRI directly [4][5][6][7]. For instance, Afshin et al explored the problem of direct estimation of LV volumes from image statistics [4].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it did not generalize well on more diverse data sets. To tackle the above-mentioned issue, Zhen et al proposed a direct and learning-based biventricular volume estimation framework [6]. The framework applied a multiscale deep network for unsupervised cardiac image representation learning from unlabeled data and then trained random forests on labeled data for biventricular volume estimation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimating left ventricular volume with ROI-based convolutional neural network

Zhu¹

2018

Turk J Elec Eng & Comp Sci

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Abstract:The volume of the human left ventricular (LV) chamber is an important indicator for diagnosing heart disease.Although LV volume can be measured manually with cardiac magnetic resonance imaging (MRI), the process is difficult and time-consuming for experienced cardiologists. This paper presents an end-to-end segmentation-free method that estimates LV volume from MRI images directly. The method initially uses Fourier transform and a regression filter to calculate the region of interest that contains the LV chambers. Then convolutional neural networks are trained to estimate the end-diastolic volume (EDV) and end-systolic volume (ESV). The resulting models accurately estimate the EDV and ESV with a mean absolute error of 15.83 and 9.82 mL, respectively, and an ejection fraction with root mean square error of 5.56%. The comparison results show that the direct estimation methods possess attractive advantages over the previous segmentation-based estimation methods.

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating left ventricular volume with ROI-based convolutional neural network

Zhu¹

2018

Turk J Elec Eng & Comp Sci

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“…The research group of Li Shuo proposed a series of methods for direct LV function indexes prediction method based on machine learning technologies [11][12][13], for example, the method based on adapted Bayesian formulation, the method based on linear support vector machine , and the method based on multiscale deep networks and regression forests.…”

Section: Introductionmentioning

confidence: 99%

A Novel Left Ventricular Volumes Prediction Method Based on Deep Learning Network in Cardiac MRI

Luo

Sun

Wang

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Napel

Jardim‐Perassi

et al. 2018

Cancer

148

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Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

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Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation

Cited by 106 publications

References 53 publications

Estimating left ventricular volume with ROI-based convolutional neural network

Estimating left ventricular volume with ROI-based convolutional neural network

A Novel Left Ventricular Volumes Prediction Method Based on Deep Learning Network in Cardiac MRI

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

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