Ventricular volume and its progression are known to be linked to several brain diseases such as dementia and schizophrenia. Therefore accurate measurement of ventricle volume is vital for longitudinal studies on these disorders, making automated ventricle segmentation algorithms desirable. In the past few years, deep neural networks have shown to outperform the classical models in many imaging domains. However, the success of deep networks is dependent on manually labeled data sets, which are expensive to acquire especially for higher dimensional data in the medical domain. In this work, we show that deep neural networks can be trained on muchcheaper-to-acquire pseudo-labels (e.g., generated by other automated less accurate methods) and still produce more accurate segmentations compared to the quality of the labels. To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation. Then on a large manually annotated test set, we show that the network significantly outperforms the conventional region growing algorithm which was used to produce the training labels for the network. Our experiments report a Dice Similarity Coefficient (DSC) of 0.874 for the trained network compared to 0.754 for the conventional region growing algorithm (p < 0.001).
The number and location of cerebral microbleeds (CMBs) in patients with traumatic brain injury (TBI) is
important to determine the severity of trauma and may hold prognostic value for patient outcome. However,
manual assessment is subjective and time-consuming due to the resemblance of CMBs to blood vessels, the
possible presence of imaging artifacts, and the typical heterogeneity of trauma imaging data. In this work, we
present a computer aided detection system based on 3D convolutional neural networks for detecting CMBs in 3D
susceptibility weighted images. Network architectures with varying depth were evaluated. Data augmentation
techniques were employed to improve the networks’ generalization ability and selective sampling was implemented
to handle class imbalance. The predictions of the models were clustered using a connected component analysis.
The system was trained on ten annotated scans and evaluated on an independent test set of eight scans. Despite
this limited data set, the system reached a sensitivity of 0.87 at 16.75 false positives per scan (2.5 false positives
per CMB), outperforming related work on CMB detection in TBI patients.
Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80.
Cerebral small vessel disease (SVD) is a disorder frequently found among the old people and is associated with deterioration in cognitive performance, parkinsonism, motor and mood impairments. White matter hyperintensities (WMH) as well as lacunes, microbleeds and subcortical brain atrophy are part of the spectrum of image findings, related to SVD. Accurate segmentation of WMHs is important for prognosis and diagnosis of multiple neurological disorders such as MS and SVD. Almost all of the published (semi-)automated WMH detection models employ multiple complex hand-crafted features, which require in-depth domain knowledge. In this paper we propose to apply a single-layer network unsupervised feature learning (USFL) method to avoid hand-crafted features, but rather to automatically learn a more efficient set of features. Experimental results show that a computer aided detection system with a USFL system outperforms a hand-crafted approach. Moreover, since the two feature sets have complementary properties, a hybrid system that makes use of both hand-crafted and unsupervised learned features, shows a significant performance boost compared to each system separately, getting close to the performance of an independent human expert.
Brain micro-bleeds (BMBs) are used as surrogate markers for detecting diffuse axonal injury in traumatic brain injury (TBI) patients. The location and number of BMBs have been shown to influence the long-term outcome of TBI. To further study the importance of BMBs for prognosis, accurate localization and quantification are required. The task of annotating BMBs is laborious, complex and prone to error, resulting in a high inter- and intra-reader variability. In this paper we propose a computer-aided detection (CAD) system to automatically detect BMBs in MRI scans of moderate to severe neuro-trauma patients. Our method consists of four steps. Step one: preprocessing of the data. Both susceptibility (SWI) and T1 weighted MRI scans are used. The images are co-registered, a brain-mask is generated, the bias field is corrected, and the image intensities are normalized. Step two: initial candidates for BMBs are selected as local minima in the processed SWI scans. Step three: feature extraction. BMBs appear as round or ovoid signal hypo-intensities on SWI. Twelve features are computed to capture these properties of a BMB. Step four: Classification. To identify BMBs from the set of local minima using their features, different classifiers are trained on a database of 33 expert annotated scans and 18 healthy subjects with no BMBs. Our system uses a leave-one-out strategy to analyze its performance. With a sensitivity of 90% and 1.3 false positives per BMB, our CAD system shows superior results compared to state-of-the-art BMB detection algorithms (developed for non-trauma patients).
Cerebral small vessel disease (SVD) is a common finding on magnetic resonance images of elderly people. White matter lesions (WML) are important markers for not only the small vessel disease, but also neuro-degenerative diseases including multiple sclerosis, Alzheimer’s disease and vascular dementia. Volumetric measurements such as the “total lesion load”, have been studied and related to these diseases. With respect to SVD we conjecture that small lesions are important, as they have been observed to grow over time and they form the majority of lesions in number. To study these small lesions they need to be annotated, which is a complex and time-consuming task. Existing (semi) automatic methods have been aimed at volumetric measurements and large lesions, and are not suitable for the detection of small lesions. In this research we established a supervised voxel classification CAD system, optimized and trained to exclusively detect small WMLs. To achieve this, several preprocessing steps were taken, which included a robust standardization of subject intensities to reduce inter-subject intensity variability as much as possible. A number of features that were found to be well identifying small lesions were calculated including multimodal intensities, tissue probabilities, several features for accurate location description, a number of second order derivative features as well as multi-scale annular filter for blobness detection. Only small lesions were used to learn the target concept via Adaboost using random forests as its basic classifiers. Finally the results were evaluated using Free-response receiver operating characteristic.
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