Resection of the piriform cortex for temporal lobe epilepsy: a Novel approach on imaging segmentation and surgical application.

BACKGROUND: The piriform cortex (PC) occupies both banks of the endorhinal sulcus and has an important role in the pathophysiology of temporal lobe epilepsy (TLE). A recent study showed that resection of more than 50% of PC increased the odds of becoming seizure free by a factor of 16. OBJECTIVE: We report the feasibility of manual segmentation of PC and application of the Geodesic Information Flows (GIF) algorithm to automated segmentation, to guide resection. METHODS: Manual segmentation of PC was performed by two blinded independent examiners in 60 patients with TLE (55% Left TLE, 52% female) with a median age of 35 years (IQR, 29-47 years) and 20 controls (60% Women) with a median age of 39.5 years (IQR, 31-49). The GIF algorithm was used to create an automated pipeline for parcellating PC which was used to guide excision as part of temporal lobe resection for TLE. RESULTS: Right PC was larger in patients and controls. Parcellation of PC was used to guide anterior temporal lobe resection, with subsequent seizure freedom and no visual field or language deficit. CONCLUSION: Reliable segmentation of PC is feasible and can be applied prospectively to guide neurosurgical resection that increases the chances of a good outcome from temporal lobe resection for TLE.

A systematic approach towards implementing value-based health care in heart failure: Understandings from retrospective analysis methods in South London.

BACKGROUND: Value-Based Health Care (VBHC) is an evolving model of healthcare delivery aimed at achieving better patient outcomes at lower costs to the healthcare provider. The practise of VBHC requires efficient information systems with good reporting capability and subsequent outcome measuring. Information systems within the National Health Service (NHS) are often multiple and not necessarily integrated to one another. We therefore developed a systematic approach to collecting, validating and analysing data from multiple sources and information systems, with the aim of designing and endorsing an automatic system to capture health outcomes data in heart failure to support future VBHC models. METHODS: A retrospective cohort of heart failure patients with reduced ejection fraction undergoing Implantable Cardioverter Defibrillator (ICD) or Cardiac Resynchronization Therapy (CRT) procedures within a limited geographical area in South London were evaluated. A purpose built database was created to integrate, transform and validate health care data from multiple information systems. RESULTS: Validation analysis shows that our implemented methodology has produced a robust dataset. Our limited cohort of 134 patients does not allow for any complex statistical analysis however has identified some important themes related to outcomes and costs. CONCLUSION: We have created a validated database specific to our Trust that can be upscaled locally with ease and transferred to other health diseases. Due to variations in local procedure from one Trust to another, this methodology now requires implementation across multiple sites to understand differences in transformation of data and outcome measuring.

Learned optical flow for intra-operative tracking of the retinal fundus.

PURPOSE: Sustained delivery of regenerative retinal therapies by robotic systems requires intra-operative tracking of the retinal fundus. We propose a supervised deep convolutional neural network to densely predict semantic segmentation and optical flow of the retina as mutually supportive tasks, implicitly inpainting retinal flow information missing due to occlusion by surgical tools. METHODS: As manual annotation of optical flow is infeasible, we propose a flexible algorithm for generation of large synthetic training datasets on the basis of given intra-operative retinal images. We evaluate optical flow estimation by tracking a grid and sparsely annotated ground truth points on a benchmark of challenging real intra-operative clips obtained from an extensive internally acquired dataset encompassing representative vitreoretinal surgical cases. RESULTS: The U-Net-based network trained on the synthetic dataset is shown to generalise well to the benchmark of real surgical videos. When used to track retinal points of interest, our flow estimation outperforms variational baseline methods on clips containing tool motions which occlude the points of interest, as is routinely observed in intra-operatively recorded surgery videos. CONCLUSIONS: The results indicate that complex synthetic training datasets can be used to specifically guide optical flow estimation. Our proposed algorithm therefore lays the foundation for a robust system which can assist with intra-operative tracking of moving surgical targets even when occluded.

T2 relaxometry in the extremely-preterm brain at adolescence.

Survival following very preterm birth is associated with cognitive and behavioral sequelae, which may have identifiable neural correlates. Many survivors of modern neonatal care in the 1990s are now young adults and the evolution of MRI findings into adult life has rarely been evaluated. We have investigated a cohort of 19-year-old adolescents without severe impairments born between 22 and 26weeks of gestation in 1995 (extremely preterm: EP). Using T2 data derived from magnetic resonance imaging we investigate differences between the brains of 46 EP participants (n=46) and the brains of a group of term-born controls (n=20). Despite EP adolescents having significantly reduced gray and white matter volumes, the composition of these tissues, assessed by both single and multi-component relaxometry, appears to be unrelated to either preterm status or gender. This may represent either insensitivity of the imaging technique or reflect that there are only subtle differences between EP subjects and their term-born peers.