Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI

Laukamp, Kai Roman; Thiele, Frank; Shakirin, Georgy; Zopfs, David; Faymonville, Andrea Maria; Timmer, Marco; Maintz, David; Perkuhn, Michael; Borggrefe, Jan

doi:10.1007/s00330-018-5595-8

Cited by 162 publications

(108 citation statements)

References 46 publications

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“…18 These methods have recently been introduced in radiotherapy for various applications, such as image segmentation, image reconstruction, image registration, treatment planning, and radiomics. [19][20][21][22][23][24][25] DLMs have been primarily proposed for pCT generation from magnetic resonance imaging (MRI). [26][27][28][29][30][31] They are particularly appealing owing to their fast computation time.…”

Section: Introductionmentioning

confidence: 99%

Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

et al. 2020

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Purpose: Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in H&N cancer radiotherapy. Methods: Forty-four patients received VMAT for H&N cancer (70-63-56 Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were: (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU-D curve method), (b) a water-airbone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CT ref). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2 mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CT ref and pCT of each method. Results: In the entire body, the MAEs and MEs of the DLM, HU-D curve method, DAM, and DIR method were 82.4 and 17.1 HU, 266.6 and 208.9 HU, 113.2 and 14.2 HU, and 95.5 and −36.6 HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon, P ≤ 0.05). The DLM dose discrepancies were 7 AE 8 cGy (maximum = 44 cGy) for the ipsilateral parotid gland D mean and 5 AE 6 cGy (max = 26 cGy) for the contralateral parotid gland mean dose (D mean). For the parotid gland D mean , no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate AE standard deviation was 98.1 AE 1.2%, 91.0 AE 5.3%, 97.9 AE 1.6%, and 98.8 AE 0.7% for the DLM, HU-D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU-D curve method, DAM, and DIR method differed significantly from those of the DLM. The mean calculation time to generate one pCT was 30 sec for the deep learning and DIR methods. Conclusions: For H&N radiotherapy, DIR method and DLM appears as the most appealing CBCTbased dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.

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Section: Introductionmentioning

confidence: 99%

Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

et al. 2020

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“…In particular, supervised learning with deep convolutional neural networks (DCNNs) is being utilized to solve many problems in MRI. Given training pairs of MR images, DCNNs have been trained to perform many challenging and clinically useful tasks, including image reconstruction of undersampled data, segmentation of structures, and synthesis of images with higher resolution or images from a different modality . Many training pairs—examples of the input and target images—are required to train the network, which effectively learns the mapping relationship between the input and target domains.…”

Section: Introductionmentioning

confidence: 99%

Conditional generative adversarial network for 3D rigid‐body motion correction in MRI

Johnson

Drangova

2019

Magnetic Resonance in Med

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westgrid.ca) and Compute Canada Calcul Canada (www.computecanada.ca).Purpose: Subject motion in MRI remains an unsolved problem; motion during image acquisition may cause blurring and artifacts that severely degrade image quality. In this work, we approach motion correction as an image-to-image translation problem, which refers to the approach of training a deep neural network to predict an image in 1 domain from an image in another domain. Specifically, the purpose of this work was to develop and train a conditional generative adversarial network to predict artifact-free brain images from motion-corrupted data.Methods: An open source MRI data set comprising T 2 *-weighted, FLASH magnitude, and phase brain images for 53 patients was used to generate complex image data for motion simulation. To simulate rigid motion, rotations and translations were applied to the image data based on randomly generated motion profiles. A conditional generative adversarial network, comprising a generator and discriminator networks, was trained using the motion-corrupted and corresponding ground truth (original) images as training pairs. Results: The images predicted by the conditional generative adversarial network have improved image quality compared to the motion-corrupted images. The mean absolute error between the motion-corrupted and ground-truth images of the test set was 16.4% of the image mean value, whereas the mean absolute error between the conditional generative adversarial network-predicted and ground-truth images was 10.8% The network output also demonstrated improved peak SNR and structural similarity index for all test-set images. Conclusion: The images predicted by the conditional generative adversarial network have quantitatively and qualitatively improved image quality compared to the motioncorrupted images. K E Y W O R D S conditional generative adversarial networks, convolutional neural networks, deep learning, motion correction, MRI

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“…The proposed method obtained a better accuracy in classifying the malignant tumor (accuracy of 99%) compared to the other existing systems. Laukamp et al 95 used a multiparametric DL model on routine MRI data in automated detection and segmentation of meningiomas in comparison to manual segmentation. The DL model yielded accurate automated detection and segmentation of meningioma tissue.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for brain diseases: A systematic review

et al. 2020

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Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using “artificial intelligence” and “brain” as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.

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Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI

Cited by 162 publications

References 46 publications

Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

Conditional generative adversarial network for 3D rigid‐body motion correction in MRI

Artificial intelligence for brain diseases: A systematic review

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