Interactive surface-guided segmentation of brain MRI data

MRI segmentation is a process of deriving semantic information from volume data. For brain MRI data, segmentation is initially performed at a voxel level and then continued at a brain surface level by generating its approximation. While successful most of the time, automated brain segmentation may leave errors which have to be removed interactively by editing individual 2D slices. We propose an approach for correcting these segmentation errors in 3D modeling space. We actively use the brain surface, which is estimated (potentially wrongly) in the automated FreeSurfer segmentation pipeline. It allows us to work with the whole data set at once, utilizing the context information and correcting several slices simultaneously. Proposed heuristic editing support and automatic visual highlighting of potential error locations allow us to substantially reduce the segmentation time. The paper describes the implementation principles of the proposed software tool and illustrates its application.     

Etiologies and predictors of readmission among obese and morbidly obese patients admitted with heart failure

Heart Failure Reviews - The relationship between severity of obesity and outcomes in heart failure (HF) has long been under debate. We studied index HF admissions from the 2013–14 National...     

A rare case of concurrent multiple primary lung cancer of different histological types

Occurrence of concurrent multiple primary malignancies with different histopathological presentations of the same organ at the same time is often not diagnosed and rarely reported in the literature. We present a case of multiple primary lung cancers with hepatic metastasis where the patient had a moderately differentiated adenocarcinoma of the right lower lobe and a moderately differentiated squamous cell carcinoma of the right upper lobe.     

Kartagener's Syndrome: A Classical Case

Background

Recurrent lower respiratory tract infection (LRTI) is a very common problem we encounter in our clinical practice. Failure to recognize the specific cause of this condition may subject the patients to unnecessary and inappropriate treatment.

Case Details

among the various causes of recurrent LRTI, the most frequent causes are abnormalities of general or local impairment of immune mechanism and abnormalities of cilia or mucus of respiratory tract. We report an adult case of recurrent upper and lower respiratory tract infections since childhood along with situs inversus totalis which was diagnosed as Kartagener''s syndrome. He had all the classical clinical and radiological features of Kartagener''s syndrome which is a rare inherited disorder which is seen in nearly half of the cases of primary cilliary dyskinesia (PCD).

Conclusion

One should always keep in mind the possibilities of Kartagener''s syndrome in patients presenting with recurrent upper and lower respiratory tract infections, sinusitis or bronchiectasis. Inability to diagnose this condition may subject the patient to unnecessary and repeated hospital admissions, investigations and treatment failure.     

Human electroencephalograms seen as fractal time series: mathematical analysis and visualization

The paper presents a novel technique of nonlinear spectral analysis, which has been used for processing encephalograms of humans. This technique is based on the concept of generalized entropy of a given probability distribution, known as the Rényi entropy that allows defining the set of generalized fractal dimensions of encephalogram (EEG) and determining fractal spectra of encephalographic signals. Unlike the Fourier spectra, the spectra of fractal dimensions contain information of both frequency and amplitude characteristics of EEG and can be used together with well-accepted techniques of EEG analysis as an enhancement of the latter. Powered by volume visualization of the brain activity, the method provides new clues for understanding the mental processes in humans.