“I’m Not Waving, I’m Drowning”: An Autoethnographical Exploration of Biographical Disruption and Reconstruction During Recovery From Prescribed Benzodiazepine Use

Benzodiazepines are group of drugs used mainly as sedatives, hypnotics, muscle relaxants, and anti-epileptics. Tapering off benzodiazepines is, for some users, a painful, traumatic, and protracted process. In this article, I use an autoethnographic approach, adopting the metaphor of water, to examine heuristically my experience of iatrogenic illness and recovery. I draw on personal journals and blog entries and former users’ narratives to consider the particular form of biographical disruption associated with benzodiazepines and the processes involved in identity reconstruction. I emphasize the role of the online community in providing benzodiazepine users such as myself with a co-cultural community through which to share a voice and make sense of our experiences. I explain how the success stories of former users provided me with the hope that I, the “medical victim,” could become the “victor” and in the process construct a new life and fresh identity.

3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.