Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J.

doi:10.18383/j.tom.2016.00166

Cited by 57 publications

(16 citation statements)

References 27 publications

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“…There are preliminary examples of the value of AI in neurology, for example in detecting structural brain lesions on MRI ( Brosch et al , 2014 ; Korfiatis et al , 2016 ; Akkus et al , 2017 ; Zaharchuk et al , 2018 ). A common limitation of clinical AI studies is the amount of available data with high-quality clinical outcome labels, rather the availability of robust AI algorithms and computational resources.…”

Section: Background—ai Emulates Human Intelligence Processed By Compmentioning

confidence: 99%

Artificial intelligence for clinical decision support in neurology

Pedersen

Verspoor

Jenkinson

et al. 2020

Brain Communications

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Abstract Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical data will lead to improved prognostic and diagnostic models in neurological disease, facilitating expert-level clinical decision tools across healthcare settings. Despite the clinical promise of artificial intelligence, machine and deep learning algorithms are not a one-size-fits-all solution for all types of clinical data and questions. In this paper, we provide an overview of the core concepts of artificial intelligence, particularly contemporary deep learning methods, to give clinician and neuroscience researchers an appreciation of how artificial intelligence can be harnessed to support clinical decisions. We clarify and emphasise the data quality and the human expertise needed to build robust clinical artificial intelligence models in neurology. Given that artificial intelligence is a rapidly evolving field, we take the opportunity to iterate important ethical principles to guide the field of medicine is it moves into an artificial intelligence enhanced future.

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Section: Background—ai Emulates Human Intelligence Processed By Compmentioning

confidence: 99%

Artificial intelligence for clinical decision support in neurology

Pedersen

Verspoor

Jenkinson

et al. 2020

Brain Communications

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“…In the field of medical image processing, deep learning approaches are providing computational solutions to a wide range of automation and classification tasks [ 2 ]. For instance, deep learning techniques have been used in organ [ 3 ] and tumor segmentation tasks [ 4 ], as well as tissue and tumor classification [ 5 , 6 ]. The fundamental difference of deep learning methods is that they take a unique approach to solving classical image processing tasks by allowing the computer to identify image features of interest.…”

Section: Introductionmentioning

confidence: 99%

Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys

et al. 2017

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Deep learning techniques are being rapidly applied to medical imaging tasks—from organ and lesion segmentation to tissue and tumor classification. These techniques are becoming the leading algorithmic approaches to solve inherently difficult image processing tasks. Currently, the most critical requirement for successful implementation lies in the need for relatively large datasets that can be used for training the deep learning networks. Based on our initial studies of MR imaging examinations of the kidneys of patients affected by polycystic kidney disease (PKD), we have generated a unique database of imaging data and corresponding reference standard segmentations of polycystic kidneys. In the study of PKD, segmentation of the kidneys is needed in order to measure total kidney volume (TKV). Automated methods to segment the kidneys and measure TKV are needed to increase measurement throughput and alleviate the inherent variability of human-derived measurements. We hypothesize that deep learning techniques can be leveraged to perform fast, accurate, reproducible, and fully automated segmentation of polycystic kidneys. Here, we describe a fully automated approach for segmenting PKD kidneys within MR images that simulates a multi-observer approach in order to create an accurate and robust method for the task of segmentation and computation of TKV for PKD patients. A total of 2000 cases were used for training and validation, and 400 cases were used for testing. The multi-observer ensemble method had mean ± SD percent volume difference of 0.68 ± 2.2% compared with the reference standard segmentations. The complete framework performs fully automated segmentation at a level comparable with interobserver variability and could be considered as a replacement for the task of segmentation of PKD kidneys by a human.

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“…For image segmentation, the most commonly utilized architectures are the fully convolutional networks [ 17 ], autoencoders [ 18 ], and UNETs [ 19 ]. These techniques have been successfully applied to segmentation of several types of medical images, including brain [ 20 , 21 ], lung [ 22 ], prostate [ 23 ], and kidney [ 24 ]. For image classification, CNNs have been the most common architecture.…”

Section: Adapting DL Models To Best Address a Class Of Problemsmentioning

confidence: 99%

Deep Learning in Radiology: Does One Size Fit All?

Erickson

Korfiatis

Kline

et al. 2018

Journal of the American College of Radiology

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100

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Deep learning (DL) is a popular method that is used to perform many important tasks in radiology and medical imaging. Some forms of DL are able to accurately segment organs (essentially, trace the boundaries, enabling volume measurements or calculation of other properties). Other DL networks are able to predict important properties from regions of an image—for instance, whether something is malignant, molecular markers for tissue in a region, even prognostic markers. DL is easier to train than traditional machine learning methods, but requires more data and much more care in analyzing results. It will automatically find the features of importance, but understanding what those features are can be a challenge. This article describes the basic concepts of DL systems and some of the traps that exist in building DL systems and how to identify those traps.

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Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning

Cited by 57 publications

References 27 publications

Artificial intelligence for clinical decision support in neurology

Artificial intelligence for clinical decision support in neurology

Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys

Deep Learning in Radiology: Does One Size Fit All?

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