Deep neural networks for anatomical brain segmentation

Brébisson, Alexandre de; Montana, Giovanni

doi:10.1109/cvprw.2015.7301312

Cited by 285 publications

(223 citation statements)

References 17 publications

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“…Computer vision and pattern recognition have already been successfully applied to computer-aided diagnosis using MRI [17,18] and for segmentation of brain structures or lesions [19][20][21]. It has also been used to assess markers of SVD qualitatively.…”

Section: Introductionmentioning

confidence: 99%

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

et al. 2017

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Correspondence: Víctor González-Castro (victor.gonzalez@ed.ac.uk) or María del C. Valdés Hernández (M. Valdes-Hernan@ed.ac.uk) In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform's coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58-0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53-0.72)) and comparable between both the observers (κ = 0.68 (0.61-0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

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Section: Introductionmentioning

confidence: 99%

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

et al. 2017

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“…For MRI T1, eight sub-cortical structures were segmented using an F-CNN model, with slices in [7] and with patches in [8]. Whole-brain segmentation with CNN using 3D patches was presented in [9] and [10]. To the best of our knowledge, this work is the first F-CNN model for whole-brain segmentation.…”

Section: Introductionmentioning

confidence: 99%

Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data

Roy

Conjeti

Sheet

et al. 2017

Lecture Notes in Computer Science

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Abstract. Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited. We propose to automatically create auxiliary labels on initially unlabeled data with existing tools and to use them for pre-training. For the subsequent fine-tuning of the network with manually labeled data, we introduce error corrective boosting (ECB), which emphasizes parameter updates on classes with lower accuracy. Furthermore, we introduce SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that combines skip connections with the unpooling strategy for upsampling. The SD-Net addresses challenges of severe class imbalance and errors along boundaries. With application to whole-brain MRI T1 scan segmentation, we generate auxiliary labels on a large dataset with FreeSurfer and finetune on two datasets with manual annotations. Our results show that the inclusion of auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D scan in 7 secs in comparison to 30 hours for the closest multi-atlas segmentation method, while reaching similar performance. It also outperforms the latest state-of-the-art F-CNN models.

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“…Ronneberger et al (2015); Moeskops et al (2016); Havaei et al (2017)), hence, we employ a CNN to automatically segment the LV myocardium. To combine the analysis of local texture with distal spatial information, multiscale CNN is used (de Brebisson and Montana, 2015;Moeskops et al, 2016;Kamnitsas et al, 2016;Havaei et al, 2017).…”

Section: Myocardium Segmentationmentioning

confidence: 99%

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Zreik

Leßmann

Hamersvelt

et al. 2018

Medical Image Analysis

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In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA).The study includes consecutively acquired CCTA scans of 166 patients who underwent invasive FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). As ischemic changes are expected to appear locally, the LV myocardium is divided into a number of spatially connected clusters, and statistics of the encodings are computed as features. Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted features.Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Twenty CCTA images were used to train the LV myocardium encoder. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 ± 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively.The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis. This might reduce the number of patients undergoing unnecessary invasive FFR measurements.

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Deep neural networks for anatomical brain segmentation

Cited by 285 publications

References 17 publications

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

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