Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network

Charron, Odelin; Lallement, Alex; Jarnet, D.; Noblet, Vincent; Clavier, Jean-Baptiste; Meyer, P.

doi:10.1016/j.compbiomed.2018.02.004

Cited by 218 publications

(181 citation statements)

References 21 publications

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“…The performances of machine-learning algorithms, including the CNNs, heavily depend on their hyperparameter settings [37]. Accordingly, some of the BM-segmentation studies, such as [10] and [11], provided a set of analyses on parameter tuning. The introduced framework's performance also relies on proper setup of multiple parameters, including (1) edge length and the block count of CropNet, (2) random gamma correction range, and (3) elastic deformation parameters, which were found empirically and individually.…”

Section: Discussionmentioning

confidence: 99%

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Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI

Dikici

Ryu

Demirer

et al. 2020

IEEE J. Biomed. Health Inform.

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Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection remains challenging partly due to their structural similarities to normal structures (e.g., vasculature). We propose a BMdetection framework using a single-sequence gadoliniumenhanced T1-weighted 3D MRI dataset. The framework focuses on detection of smaller (< 15 mm) BM lesions and consists of: (1) candidate-selection stage, using Laplacian of Gaussian approach for highlighting parts of a MRI volume holding higher BM occurrence probabilities, and (2) detection stage that iteratively processes cropped region-of-interest volumes centered by candidates using a custom-built 3D convolutional neural network ("CropNet"). Data is augmented extensively during training via a pipeline consisting of random gamma correction and elastic deformation stages; the framework thereby maintains its invariance for a plausible range of BM shape and intensity representations. This approach is tested using five-fold cross-validation on 217 datasets from 158 patients, with training and testing groups randomized per patient to eliminate learning bias. The BM database included lesions with a mean diameter of ~5.4 mm and a mean volume of ~160 mm 3 . For 90% BM-detection sensitivity, the framework produced on average 9.12 falsepositive BM detections per patient (standard deviation of 3.49); for 85% sensitivity, the average number of falsepositives declined to 5.85. Comparative analysis showed that the framework produces comparable BM-detection accuracy with the state-of-art approaches validated for significantly larger lesions.Index Terms-magnetic resonance imaging, brain metastases, convolutional neural networks, deep learning, scale-space representations, computer-aided detection, medical image analysis.

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Section: Discussionmentioning

confidence: 99%

“…sensitivity vs average number of false-positive detections per patient -AFP) at various output threshold settings (~0 -low likelihood and ~1 -high likelihood of metastasis). Accordingly, state-of-art approaches[10] [11][15] follow a similar reporting methodology.…”

Section: Evaluation Metricmentioning

confidence: 99%

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI

Dikici

Ryu

Demirer

et al. 2020

IEEE J. Biomed. Health Inform.

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“…While the current benchmark for brain metastases segmentation employs MRI imaging only [1,6], CT imaging is an essential reference for clinical treatment planning due to its spatial accuracy. We thus proposed a deep learning framework using multimodal imaging (MRI+CT) and ensemble neural networks for brain metastases detection and segmentation.…”

Section: Deep Learning Frameworkmentioning

confidence: 99%

“…DeepMedic DeepMedic [4] is originally designed for brain tumor segmentation on multi-channel MRI and also had been applied to BMs [1,6]. It consists of multiple parallel pathways-one branch takes small patches from full resolution images as input and the others utilizes subsampled-version of the images.…”

Section: Deep Learning Frameworkmentioning

confidence: 99%

“…However, such manually contouring process can be very time-consuming and suffer from large inter and intra-reader variability [11]. Driven by the ever-increasing capability of deep learning, automated segmentation of BMs using neural networks has been recently proposed [1,6]. Previous works on computer-aided segmentation of BMs used only MRI as an imaging input.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

Weng

et al. 2019

Artificial Intelligence in Radiation Therapy

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Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very effective, SRS currently requires manually intensive delineation of tumors. In this work, we present a deep learning approach for automated detection and segmentation of brain metastases using multimodal imaging and ensemble neural networks. In order to address small and multiple brain metastases, we further propose a volume-aware Dice loss which optimizes model performance using the information of lesion size. This work surpasses current benchmark levels and demonstrates a reliable AI-assisted system for SRS treatment planning for multiple brain metastases.

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The Role of Big Data in Personalized Medicine

Bibault

Liu

2020

Precision Medicine in Oncology

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Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network

Cited by 218 publications

References 21 publications

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI

Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

The Role of Big Data in Personalized Medicine

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