Extraction of brain tissue from CT head images using fully convolutional neural networks

Akkus, Zeynettin; Kostandy, Petro M.; Philbrick, Kenneth A.; Erickson, Bradley J.

doi:10.1117/12.2293423

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…Newer AI systems may perform these tasks at even greater speed and scale, potentially allowing for image superresolution 54 as well as immediate automated segmentation of organs of interest. [55][56][57] Early research also demonstrates promise in synthetic modality transfer, that is the creation of a CT image from an MRI scan or vice versa, 55 obviating the need for a second imaging procedure entirely.…”

Section: Image Processingmentioning

confidence: 99%

Artificial intelligence in diagnostic imaging: impact on the radiography profession

Hardy

Harvey

2019

The British Journal of Radiology

109

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The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.

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Section: Image Processingmentioning

confidence: 99%

Artificial intelligence in diagnostic imaging: impact on the radiography profession

Hardy

Harvey

2019

The British Journal of Radiology

109

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“…An example of this algorithm performance on a 5 mm slice, non-contrast head CT with a soft-tissue convolution kernel is seen in Figure 3, which extracts the relevant areas for analysis. Recently, convolutional neural networks and shape propagation techniques have been quite successful in this task (Akkus et al, 2018) and models have been released (https://github.com/aqqush/CT_BET). Overall, much research can still be done in this area as traumatic brain injury (TBI) and surgery, such as craniotomies or craniectomies, can cause these methods to potentially fail.…”

Section: Data Preprocessingmentioning

confidence: 99%

Recommendations for Processing Head CT Data

Muschelli

2019

Front. Neuroinform.

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Many research applications of neuroimaging use magnetic resonance imaging (MRI). As such, recommendations for image analysis and standardized imaging pipelines exist. Clinical imaging, however, relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Currently, there is only one image processing pipeline for head CT, which focuses mainly on head CT data with lesions. We present tools and a complete pipeline for processing CT data, focusing on open-source solutions, that focus on head CT but are applicable to most CT analyses. We describe going from raw DICOM data to a spatially normalized brain within CT presenting a full example with code. Overall, we recommend anonymizing data with Clinical Trials Processor, converting DICOM data to NIfTI using dcm2niix, using BET for brain extraction, and registration using a publicly-available CT template for analysis.

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“…Compared with CT-BET, which uses a full convolutional neural network to segment CT brain tissue, this method achieved an approximate accuracy rate when its amount of training parameters was much less than that of CT-BET. 13 This result could be sufficient to improve the efficiency and accuracy in clinical diagnosis and treatment process with the extracted pathological characteristics such as size and shape of the hemorrhage. This method could offer quantitative data in calculation of the area of bleeding.…”

Section: Model Trainingmentioning

confidence: 99%

Artificial intelligence-based identification of brain CT medical images

Chen

Cai²,

Wu³

et al. 2023

AOPC 2022: Biomedical Optics

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Stroke is a group of diseases with severe brain tissue damage, which are caused by either the sudden rupture of brain blood vessels (cerebral hemorrhage) or brain blood vessel obstruction, leading to rapid changes and high mortality. The diagnosis of stroke mainly relies on medical imaging techniques, including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which require experienced radiologists to guarantee suitable accuracy. However, the amount of brain CT image data is extremely large, usually exceeding the technical capabilities of radiologists. Currently, artificial intelligence has been applied into CT image analysis in order to achieve high sensitivity and specific diagnosis results for clinical examinations. In this work, we obtained CT images from a database (CQ500), including epidural hemorrhage, cerebral parenchymal hemorrhage and intraventricular hemorrhage. Then, we introduced a deep-learning algorithm based on U-Net model, which was trained to generate image segmentation, providing a calculated accuracy of prediction yield. The results showed that the average intersection ratio of the final model on the test set could reach the value of 0.96. Briefly, artificial intelligence in this work can efficiently improve the analysis of brain CT images, suggesting an important development direction for future medical imaging auxiliary diagnosis.

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Extraction of brain tissue from CT head images using fully convolutional neural networks

Cited by 12 publications

References 14 publications

Artificial intelligence in diagnostic imaging: impact on the radiography profession

Artificial intelligence in diagnostic imaging: impact on the radiography profession

Recommendations for Processing Head CT Data

Artificial intelligence-based identification of brain CT medical images

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