Classifying MRI motion severity using a stacked ensemble approach

Mohebbian, Mohammad Reza; Walia, Ekta; Habibullah, Mohammad; Stapleton, Shawn; Wahid, Khan A.

doi:10.1016/j.mri.2020.10.007

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…The presence of motion artifacts in images is attributed to complex interactions between image structure, motion patterns, details of MRI pulse sequences, and k-space sampling techniques (Mohebbian et al 2021). We simulate MRI images with motion artifacts caused by rigid body motion affecting the entire brain, and the procedure shown in supplementary-figure 1.…”

Section: Simulate Motion Artifactmentioning

confidence: 99%

Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation

Qu,

Lu,

Shi

et al. 2024

Phys. Med. Biol.

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MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors’ workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to the high cost involved in annotating medical images, the dataset available for training fully supervised methods is very limited. Additionally, medical images are prone to noise and motion artifacts, negatively impacting quality. In this work, we propose MAPSS, a motion-artifact-augmented pseudo-label network for semi-supervised segmentation. Our method combines motion artifact data augmentation with the pseudo-label semi-supervised training framework. We conduct several experiments under different semi-supervised settings on a publicly available dataset BraTS2020 for brain tumor segmentation. The experimental results show that MAPSS achieves accurate brain tumor segmentation with only a small amount of labeled data and maintains robustness in motion-artifact-influenced images. We also assess the generalization performance of MAPSS using the Left Atrium dataset. Our algorithm is of great significance for assisting doctors in formulating treatment plans and improving treatment quality.

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Section: Simulate Motion Artifactmentioning

confidence: 99%

Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation

Qu,

Lu,

Shi

et al. 2024

Phys. Med. Biol.

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“…As with analysis by agroecosystem, all best-fit models for the combined datasets were Stacked Ensemble-type models. As discussed previously, Stacked Ensemble is a machine learning method that combines multiple learning methods (i.e., GBM and XGBoost) by using the output of one model as the input for another (Rajadurai and Gandhi, 2020;Mohebbian et al, 2021). Stacked Ensemble is a robust approach that can work with many data types and uses (Zai and Rajadurai and Gandhi, 2020).…”

Section: Comparison Of All Site Datamentioning

confidence: 99%

Machine learning algorithms improve MODIS GPP estimates in United States croplands

Menefee,

Lee,

Flynn

et al. 2023

Front. Remote Sens.

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Introduction: Machine learning methods combined with satellite imagery have the potential to improve estimates of carbon uptake of terrestrial ecosystems, including croplands. Studying carbon uptake patterns across the U.S. using research networks, like the Long-Term Agroecosystem Research (LTAR) network, can allow for the study of broader trends in crop productivity and sustainability.Methods: In this study, gross primary productivity (GPP) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) for three LTAR cropland sites were integrated for use in a machine learning modeling effort. They are Kellogg Biological Station (KBS, 2 towers and 20 site-years), Upper Mississippi River Basin (UMRB - Rosemount, 1 tower and 12 site-years), and Platte River High Plains Aquifer (PRHPA, 3 towers and 52 site-years). All sites were planted to maize (Zea mays L.) and soybean (Glycine max L.). The MODIS GPP product was initially compared to in-situ measurements from Eddy Covariance (EC) instruments at each site and then to all sites combined. Next, machine learning algorithms were used to create refined GPP estimates using air temperature, precipitation, crop type (maize or soybean), agroecosystem, and the MODIS GPP product as inputs. The AutoML program in the h2o package tested a variety of individual and combined algorithms, including Gradient Boosting Machines (GBM), eXtreme Gradient Boosting Models (XGBoost), and Stacked Ensemble.Results and discussion: The coefficient of determination (r2) of the raw comparison (MODIS GPP to EC GPP) was 0.38, prior to machine learning model incorporation. The optimal model for simulating GPP across all sites was a Stacked Ensemble type with a validated r2 value of 0.87, RMSE of 2.62 units, and MAE of 1.59. The machine learning methodology was able to successfully simulate GPP across three agroecosystems and two crops.

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“…2 The results were promising, as we achieved highly accurate detection of severe motion artefacts, with a balanced accuracy exceeding 80%, which closely matches the performance of human annotators . While the use of synthetic motion to detect real artefacts in MRIs had already been studied, [3][4][5] the simulation of noise or low contrast has mainly been used for data augmentation purposes 6 rather than for artefact detection tasks.…”

Section: Introductionmentioning

confidence: 99%

Leveraging noise and contrast simulation for the automatic quality control of routine clinical T1-weighted brain MRI

Loizillon,

Mabille,

Bottani

et al. 2024

Medical Imaging 2024: Image Processing

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The recent advent of clinical data warehouses (CDWs) has facilitated the sharing of very large volumes of medical data for research purposes. MRIs can be affected by various artefacts such as motion, noise or poor contrast that can severely degrade the overall quality of an image. In CDWs, a large amount of MRIs are unusable because corrupted by these diverse artefacts. Given the huge number of MRIs present in CDWs, manually detecting these artefacts becomes an impractical task. Therefore, it is necessary to develop an automated tool that can efficiently identify and exclude corrupted images. We previously proposed an approach for the detection of motion artefacts in 3D T1-weighted brain MRIs. In this paper, we propose to extend our work to two other types of artefacts: poor contrast and noise. We rely on a transfer learning approach, which leverages synthetic artefact generation, and comprises two steps: model pre-training on research data using synthetic artefacts, followed by a fine-tuning step, where we generalise the pre-trained models to clinical routine data relying on the manual labelling of 5000 images. The main objectives of our study were two-fold: to be able to exclude images with severe artefacts and to detect moderate artefacts. Our approach excelled in meeting the first objective, achieving a balanced accuracy of over 84% for the detection of severe noise and very poor contrast, which closely matched the performance of human annotators. Nevertheless, performance in the pursuit of the second objective was less satisfactory and inferior to that of the human annotators. Overall, our framework will be useful for taking full advantage of MRIs present in CDWs.

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Classifying MRI motion severity using a stacked ensemble approach

Cited by 11 publications

References 22 publications

Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation

Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation

Machine learning algorithms improve MODIS GPP estimates in United States croplands

Leveraging noise and contrast simulation for the automatic quality control of routine clinical T1-weighted brain MRI

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