Medicine (Austin & Northern Health) - Research Publications

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Author: Connelly, Alan × End date: 2014 × Start date: 2014 ×

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    Lesion segmentation from multimodal MRI using random forest following ischemic stroke
    Mitra, J ; Bourgeat, P ; Fripp, J ; Ghose, S ; Rose, S ; Salvado, O ; Connelly, A ; Campbell, B ; Palmer, S ; Sharma, G ; Christensen, S ; Carey, L (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2014-09)
    Understanding structure-function relationships in the brain after stroke is reliant not only on the accurate anatomical delineation of the focal ischemic lesion, but also on previous infarcts, remote changes and the presence of white matter hyperintensities. The robust definition of primary stroke boundaries and secondary brain lesions will have significant impact on investigation of brain-behavior relationships and lesion volume correlations with clinical measures after stroke. Here we present an automated approach to identify chronic ischemic infarcts in addition to other white matter pathologies, that may be used to aid the development of post-stroke management strategies. Our approach uses Bayesian-Markov Random Field (MRF) classification to segment probable lesion volumes present on fluid attenuated inversion recovery (FLAIR) MRI. Thereafter, a random forest classification of the information from multimodal (T1-weighted, T2-weighted, FLAIR, and apparent diffusion coefficient (ADC)) MRI images and other context-aware features (within the probable lesion areas) was used to extract areas with high likelihood of being classified as lesions. The final segmentation of the lesion was obtained by thresholding the random forest probabilistic maps. The accuracy of the automated lesion delineation method was assessed in a total of 36 patients (24 male, 12 female, mean age: 64.57±14.23yrs) at 3months after stroke onset and compared with manually segmented lesion volumes by an expert. Accuracy assessment of the automated lesion identification method was performed using the commonly used evaluation metrics. The mean sensitivity of segmentation was measured to be 0.53±0.13 with a mean positive predictive value of 0.75±0.18. The mean lesion volume difference was observed to be 32.32%±21.643% with a high Pearson's correlation of r=0.76 (p<0.0001). The lesion overlap accuracy was measured in terms of Dice similarity coefficient with a mean of 0.60±0.12, while the contour accuracy was observed with a mean surface distance of 3.06mm±3.17mm. The results signify that our method was successful in identifying most of the lesion areas in FLAIR with a low false positive rate.
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    Mutations in Mammalian Target of Rapamycin Regulator DEPDC5 Cause Focal Epilepsy with Brain Malformations
    Scheffer, IE ; Heron, SE ; Regan, BM ; Mandelstam, S ; Crompton, DE ; Hodgson, BL ; Licchetta, L ; Provini, F ; Bisulli, F ; Vadlamudi, L ; Gecz, J ; Connelly, A ; Tinuper, P ; Ricos, MG ; Berkovic, SF ; Dibbens, LM (WILEY-BLACKWELL, 2014-05)
    We recently identified DEPDC5 as the gene for familial focal epilepsy with variable foci and found mutations in >10% of small families with nonlesional focal epilepsy. Here we show that DEPDC5 mutations are associated with both lesional and nonlesional epilepsies, even within the same family. DEPDC5-associated malformations include bottom-of-the-sulcus dysplasia (3 members from 2 families), and focal band heterotopia (1 individual). DEPDC5 negatively regulates the mammalian target of rapamycin (mTOR) pathway, which plays a key role in cell growth. The clinicoradiological phenotypes associated with DEPDC5 mutations share features with the archetypal mTORopathy, tuberous sclerosis, raising the possibility of therapies targeted to this pathway.