Towards Portable Large-Scale Image Processing with High-Performance Computing

Huo, Yuankai; Blaber, Justin A.; Damon, Stephen; Boyd, Brian D.; Bao, Shunxing; Parvathaneni, Prasanna; Noguera, Camilo Bermudez; Chaganti, Shikha; Nath, Vishwesh; Greer, Jasmine M.; Lyu, Ilwoo; French, William R.; Newton, Allen T.; Rogers, Baxter P.; Landman, Bennett A.

doi:10.1007/s10278-018-0080-0

Cited by 29 publications

(10 citation statements)

References 31 publications

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“…For instance, if each scan needs 36 hours using multi-atlas segmentation, 21 computational years are required for a single workstation. Meanwhile, the large-scale medical image processing infrastructure (Huo et al, 2018a) and high performance computing cluster at Vanderbilt University were used. These resources access more than 10,000 computational cores.…”

Section: Discussionmentioning

confidence: 99%

3D whole brain segmentation using spatially localized atlas network tiles

et al. 2019

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Detailed whole brain segmentation is an essential quantitative technique in medical image analysis, which provides a non-invasive way of measuring brain regions from a clinical acquired structural magnetic resonance imaging (MRI). Recently, deep convolution neural network (CNN) has been applied to whole brain segmentation. However, restricted by current GPU memory, 2D based methods, downsampling based 3D CNN methods, and patch-based high-resolution 3D CNN methods have been the de facto standard solutions. 3D patch-based high resolution methods typically yield superior performance among CNN approaches on detailed whole brain segmentation (>100 labels), however, whose performance are still commonly inferior compared with state-of-the-art multi-atlas segmentation methods (MAS) due to the following challenges: (1) a single network is typically used to learn both spatial and contextual information for the patches, (2) limited manually traced whole brain volumes are available (typically less than 50) for training a network. In this work, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks (FCN) for high-resolution whole brain segmentation. To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed

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Section: Discussionmentioning

confidence: 99%

3D whole brain segmentation using spatially localized atlas network tiles

et al. 2019

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“…Brain age calculations were performed on an NVIDIA GeForce Titan GPU with 12 GB memory and all deep learning algorithms were implemented and tested using Tensorflow v1.4 with a Keras backend v2.2. In order to analyze this cohort, we used a large-scale medical image processing infrastructure 38 and high performance computing cluster at Vanderbilt University. Trained models and analysis code for the BAG prediction used by Bermudez et al 8 .…”

Section: Methodsmentioning

confidence: 99%

“…2. In order to analyze this cohort, we used a large-scale medical image processing infrastructure 38 and high performance computing cluster at Vanderbilt University. Trained models and analysis code for the BAG prediction used by Bermudez et al 8 .…”

Section: Mri Analyses and Calculation Of Brain Agementioning

confidence: 99%

Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression

et al. 2020

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Depression is associated with markers of accelerated aging, but it is unclear how this relationship changes across the lifespan. We examined whether a brain-based measure of accelerated aging differed between depressed and never-depressed subjects across the adult lifespan and whether it was related to cognitive performance and disability. We applied a machine-learning approach that estimated brain age from structural MRI data in two depressed cohorts, respectively 170 midlife adults and 154 older adults enrolled in studies with common entry criteria. Both cohorts completed broad cognitive batteries and the older subgroup completed a disability assessment. The machine-learning model estimated brain age from MRI data, which was compared to chronological age to determine the brain–age gap (BAG; estimated age-chronological age). BAG did not differ between midlife depressed and nondepressed adults. Older depressed adults exhibited significantly higher BAG than nondepressed elders (Wald χ2 = 8.84, p = 0.0029), indicating a higher estimated brain age than chronological age. BAG was not associated with midlife cognitive performance. In the older cohort, higher BAG was associated with poorer episodic memory performance (Wald χ2 = 4.10, p = 0.0430) and, in the older depressed group alone, slower processing speed (Wald χ2 = 4.43, p = 0.0354). We also observed a statistical interaction where greater depressive symptom severity in context of higher BAG was associated with poorer executive function (Wald χ2 = 5.89, p = 0.0152) and working memory performance (Wald χ2 = 4.47, p = 0.0346). Increased BAG was associated with greater disability (Wald χ2 = 6.00, p = 0.0143). Unlike midlife depression, geriatric depression exhibits accelerated brain aging, which in turn is associated with cognitive and functional deficits.

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“…As a result, the prevalent 2D based image retrieval methods developed in computer vision communities are able to be applied to medical images retrieval directly using the dMIR pipeline. In the future, the proposed method could be used to solve the quality assurance for large-scale image archives, such as Vanderbilt VUIIS-CCI XNAT [11].…”

Section: Discussionmentioning

confidence: 99%

Montage based 3D medical image retrieval from traumatic brain injury cohort using deep convolutional neural network

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Huo

Chaganti

et al. 2019

Medical Imaging 2019: Image Processing

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Brain imaging analysis on clinically acquired computed tomography (CT) is essential for the diagnosis, risk prediction of progression, and treatment of the structural phenotypes of traumatic brain injury (TBI). However, in real clinical imaging scenarios, entire body CT images (e.g., neck, abdomen, chest, pelvis) are typically captured along with whole brain CT scans. For instance, in a typical sample of clinical TBI imaging cohort, only ~15% of CT scans actually contain whole brain CT images suitable for volumetric brain analyses; the remaining are partial brain or non-brain images. Therefore, a manual image retrieval process is typically required to isolate the whole brain CT scans from the entire cohort. However, the manual image retrieval is time and resource consuming and even more difficult for the larger cohorts. To alleviate the manual efforts, in this paper we propose an automated 3D medical image retrieval pipeline, called deep montage-based image retrieval (dMIR), which performs classification on 2D montage images via a deep convolutional neural network. The novelty of the proposed method for image processing is to characterize the medical image retrieval task based on the montage images. In a cohort of 2000 clinically acquired TBI scans, 794 scans were used as training data, 206 scans were used as validation data, and the remaining 1000 scans were used as testing data. The proposed achieved accuracy=1.0, recall=1.0, precision=1.0, f1=1.0 for validation data, while achieved accuracy=0.988, recall=0.962, precision=0.962, f1=0.962 for testing data. Thus, the proposed dMIR is able to perform accurate CT whole brain image retrieval from large-scale clinical cohorts.

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Towards Portable Large-Scale Image Processing with High-Performance Computing

Cited by 29 publications

References 31 publications

3D whole brain segmentation using spatially localized atlas network tiles

3D whole brain segmentation using spatially localized atlas network tiles

Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression

Montage based 3D medical image retrieval from traumatic brain injury cohort using deep convolutional neural network

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