Atlas-based Segmentation of Tumor-bearing Brain Images

In diagnostic neuroradiology as well as in radiation oncology and neurosurgery, there is an increasing demand for accurate segmentation of tumor-bearing brain images. Atlas-based segmentation is an appealing automatic technique thanks to its robustness and versatility. However, atlas-based segmentation of tumor-bearing brain images is challenging due to the confounding effects of the tumor in the patient image. In this article, we provide a brief background on brain tumor imaging and introduce the clinical perspective, before we categorize and review the state of the art in the current literature on atlas-based segmentation for tumor-bearing brain images. We also present selected methods and results from our own research in more detail. Finally, we conclude with a short summary and look at new developments in the field, including requirements for future routine clinical use.

Electrochemical simulation of phase I metabolism for 21 drugs using four different working electrodes in an automated screening setup with MS detection.

BACKGROUND Electrochemical conversion of xenobiotics has been shown to mimic human phase I metabolism for a few compounds. MATERIALS & METHODS Twenty-one compounds were analyzed with a semiautomated electrochemical setup and mass spectrometry detection. RESULTS The system was able to mimic some metabolic pathways, such as oxygen gain, dealkylation and deiodination, but many of the expected and known metabolites were not produced. CONCLUSION Electrochemical conversion is a useful approach for the preparative synthesis of some types of metabolites, but as a screening method for unknown phase I metabolites, the method is, in our opinion, inferior to incubation with human liver microsomes and in vivo experiments with laboratory animals, for example.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Interactive Segmentation of MR Images from Brain Tumor Patients

Medical doctors often do not trust the result of fully automatic segmentations because they have no possibility to make corrections if necessary. On the other hand, manual corrections can introduce a user bias. In this work, we propose to integrate the possibility for quick manual corrections into a fully automatic segmentation method for brain tumor images. This allows for necessary corrections while maintaining a high objectiveness. The underlying idea is similar to the well-known Grab-Cut algorithm, but here we combine decision forest classification with conditional random field regularization for interactive segmentation of 3D medical images. The approach has been evaluated by two different users on the BraTS2012 dataset. Accuracy and robustness improved compared to a fully automatic method and our interactive approach was ranked among the top performing methods. Time for computation including manual interaction was less than 10 minutes per patient, which makes it attractive for clinical use.

Feasibility of automated slope III and Scond analysis in children.

BACKGROUND Multiple breath washout (MBW) derived Scond is an established index of ventilation inhomogeneity. Time-consuming post hoc calculations of the expirogram's slope of alveolar phase III (SIII) and the lack of available software hampered widespread application of Scond. METHODS Seventy-two school-aged children (45 with cystic fibrosis; CF) performed 3 nitrogen MBW. We tested a new automated algorithm for Scond analysis (Scondauto ) which comprised breath selection for SIII detection, calculation and reporting of test quality. We compared Scondauto to (i) standard Scond analysis (Scondmanual ) with manual breath selection and to (ii) pragmatic Scond analysis including all breaths (Scondall ). Primary outcomes were success rate and agreement between different Scond protocols, and Scond fitting quality (linear regression R(2) ). RESULTS Average Scondauto (0.06 for CF and 0.01 for controls) was not different from Scondmanual (0.06 for CF and 0.01 for controls) and showed comparable fitting quality (R(2) 0.53 for CF and 0.13 for controls vs. R(2) 0.54 for CF and 0.13 for controls). Scondall was similar in CF and controls but with inferior fitting quality compared to Scondauto and Scondmanual . CONCLUSIONS Automated Scond calculation is feasible and produces robust results comparable to the standard manual way of Scond calculation. This algorithm provides a valid, fast and objective tool for regular use, even in children. Pediatr Pulmonol. © 2014 Wiley Periodicals, Inc.

Late cardiac sodium current can be assessed using automated patch-clamp

The cardiac late Na (+) current is generated by a small fraction of voltage-dependent Na (+) channels that undergo a conformational change to a burst-gating mode, with repeated openings and closures during the action potential (AP) plateau. Its magnitude can be augmented by inactivation-defective mutations, myocardial ischemia, or prolonged exposure to chemical compounds leading to drug-induced (di)-long QT syndrome, and results in an increased susceptibility to cardiac arrhythmias. Using CytoPatch™ 2 automated patch-clamp equipment, we performed whole-cell recordings in HEK293 cells stably expressing human Nav1.5, and measured the late Na (+) component as average current over the last 100 ms of 300 ms depolarizing pulses to -10 mV from a holding potential of -100 mV, with a repetition frequency of 0.33 Hz. Averaged values in different steady-state experimental conditions were further corrected by the subtraction of current average during the application of tetrodotoxin (TTX) 30 μM. We show that ranolazine at 10 and 30 μM in 3 min applications reduced the late Na (+) current to 75.0 ± 2.7% (mean ± SEM, n = 17) and 58.4 ± 3.5% ( n = 18) of initial levels, respectively, while a 5 min application of veratridine 1 μM resulted in a reversible current increase to 269.1 ± 16.1% ( n = 28) of initial values. Using fluctuation analysis, we observed that ranolazine 30 μM decreased mean open probability p from 0.6 to 0.38 without modifying the number of active channels n, while veratridine 1 μM increased n 2.5-fold without changing p. In human iPSC-derived cardiomyocytes, veratridine 1 μM reversibly increased APD90 2.12 ± 0.41-fold (mean ± SEM, n = 6). This effect is attributable to inactivation removal in Nav1.5 channels, since significant inhibitory effects on hERG current were detected at higher concentrations in hERG-expressing HEK293 cells, with a 28.9 ± 6.0% inhibition (mean ± SD, n = 10) with 50 μM veratridine.

The Multimodal Brain TumorImage Segmentation Benchmark (BRATS)

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all subregions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

Spectral-domain optical coherence tomography findings after severe exogenous endophthalmitis

PURPOSE To report outcomes and assess structural changes in the retina in patients with severe endophthalmitis. METHODS Retrospective, nonrandomized, interventional case series at a tertiary referral centre. Spectral domain optical coherence tomography (OCT) images of both eyes were acquired at least 5 months after pars plana vitrectomy. OCT images were analyzed using retinal layer segmentation. RESULTS Nine patients (46-80 years of age) were included in this study. Average ETDRS visual acuity before treatment was 23 letters and improved to 74 letters. In our cohort we did not find a generalized reduction of retinal layers using automated layer segmentation. CONCLUSION Our findings suggest that prompt treatment of severe endophthalmitis with intravitreal antibiotics followed by pars plana vitrectomy may lead to excellent visual outcomes with minimal damage to the retinal architecture.