Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI

Grøvik, Endre; Yi, Darvin; Michael, Cassia; Tong, Elisabeth; Rubin, Daniel L.; Zaharchuk, Greg

doi:10.1002/jmri.26766

Cited by 181 publications

(194 citation statements)

References 34 publications

Supporting

Mentioning

168

Contrasting

Order By: Relevance

“…Although brain metastases are usually well-circumscribed contrast-enhancing lesions, the manual, three-dimensional segmentation is time-consuming. To overcome this issue, machine learning techniques are being developed for the automated detection and segmentation of brain metastases using deep learning (31,32). However, these tools still have to prove their reliability and added value to ultimately become part of clinical routine.…”

Section: Segmentationmentioning

confidence: 99%

PET/MRI Radiomics in Patients With Brain Metastases

et al. 2020

View full text Add to dashboard Cite

Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data. The growing availability of high-performance computing allows the extraction of quantitative imaging features from medical images that are usually beyond human perception. Using machine learning techniques and advanced statistical methods, subsets of such imaging features are used to generate mathematical models that represent characteristic signatures related to the underlying tumor biology and might be helpful for the assessment of prognosis or treatment response, or the identification of molecular markers. The identification of appropriate, characteristic image features as well as the generation of predictive or prognostic mathematical models is summarized under the term radiomics. This review summarizes the current status of radiomics in patients with brain metastases.

show abstract

Section: Segmentationmentioning

confidence: 99%

PET/MRI Radiomics in Patients With Brain Metastases

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The first application which produced state-of-the-art results in automated segmentation of BM in MRI was published in 2015 by Losch et al [5]. Since then, a large variety of network architectures for deep learning including GoogLeNet [6], CropNet [7], DeepMedic [8] and En-DeepMedic [9] have been tested. A common limitation is the high number of false positives and the small sample sizes used for training.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

et al. 2020

View full text Add to dashboard Cite

Introduction: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of deep convolutional neural networks (DCNN) in automatic segmentation of brain metastases (BM). Methods: A conventional U-Net (cU-Net), a modified U-Net (moU-Net) and a U-Net trained only on BM smaller than 0.4 ml (sU-Net) were implemented. Performance was assessed on a separate test set employing sensitivity, specificity, average false positive rate (AFPR), the dice similarity coefficient (DSC), Bland-Altman analysis and the concordance correlation coefficient (CCC). Results: A dataset of 509 patients (1223 BM) was split into a training set (469 pts) and a test set (40 pts). A combination of all trained networks was the most sensitive (0.82) while maintaining a specificity 0.83. The same model achieved a sensitivity of 0.97 and a specificity of 0.94 when considering only lesions larger than 0.06 ml (75% of all lesions). Type of primary cancer had no significant influence on the mean DSC per lesion (p = 0.60). Agreement between manually and automatically assessed tumor volumes as quantified by a CCC of 0.87 (95% CI, 0.77-0.93), was excellent. Conclusion: Using a dataset which properly captured the variation in imaging appearance observed in clinical practice, we were able to conclude that DCNNs reach clinically relevant performance for most lesions. Clinical applicability is currently limited by the size of the target lesion. Further studies should address if small targets are accurately represented in the test data.

show abstract

“…The system yielded an average Dice similarity coefficient of 0.67, where the detection false-positive rate in connection to the sensitivity percentage is not reported. More recently, Grøvik et al [15] demonstrated the usage of 2.5D fully CNN, based on GoogLeNet architecture [16], for detection and segmentation of BM. Their solution utilized multiple sequences of MRI for each patient: T1-weighted 3D fast spin-echo (CUBE), post-contrast T1-weighted 3D axial IR-prepped FSPGR, and 3D CUBE fluid-attenuated inversion recovery.…”

mentioning

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.

View full text Add to dashboard Cite

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.

show abstract

Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI

Cited by 181 publications

References 34 publications

PET/MRI Radiomics in Patients With Brain Metastases

PET/MRI Radiomics in Patients With Brain Metastases

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

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

Contact Info

Product

Resources

About