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.
Worldwide, 99% of all maternal deaths occur in low-resource countries. Ultrasound imaging can be used to detect maternal risk factors, but requires a well-trained sonographer to obtain the biometric parameters of the fetus. One of the most important biometric parameters is the fetal Head Circumference (HC). The HC can be used to estimate the Gestational Age (GA) and assess the growth of the fetus. In this paper we propose a method to estimate the fetal HC with the use of the Obstetric Sweep Protocol (OSP). With the OSP the abdomen of pregnant women is imaged with the use of sweeps. These sweeps can be taught to somebody without any prior knowledge of ultrasound within a day. Both the OSP and the standard two-dimensional ultrasound image for HC assessment were acquired by an experienced gynecologist from fifty pregnant women in St. Luke’s Hospital in Wolisso, Ethiopia. The reference HC from the standard two-dimensional ultrasound image was compared to both the manually measured HC and the automatically measured HC from the OSP data. The median difference between the estimated GA from the manual measured HC using the OSP and the reference standard was -1.1 days (Median Absolute Deviation (MAD) 7.7 days). The median difference between the estimated GA from the automatically measured HC using the OSP and the reference standard was -6.2 days (MAD 8.6 days). Therefore, it can be concluded that it is possible to estimate the fetal GA with simple obstetric sweeps with a deviation of only one week.
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).
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