Sensorless cardiac phase detection for synchronized control of ventricular assist devices using nonlinear kernel regression model.

Recently, driving methods for synchronizing ventricular assist devices (VADs) with heart rhythm of patients suffering from severe heart failure have been receiving attention. Most of the conventional methods require implanting a sensor for measurement of a signal, such as electrocardiogram, to achieve synchronization. In general, implanting sensors into the cardiovascular system of the patients is undesirable in clinical situations. The objective of this study was to extract the heartbeat component without any additional sensors, and to synchronize the rotational speed of the VAD with this component. Although signals from the VAD such as the consumption current and the rotational speed are affected by heartbeat, these raw signals cannot be utilized directly in the heartbeat synchronization control methods because they are changed by not only the effect of heartbeat but also the change in the rotational speed itself. In this study, a nonlinear kernel regression model was adopted to estimate the instantaneous rotational speed from the raw signals. The heartbeat component was extracted by computing the estimation error of the model with parameters determined by using the signals when there was no effect of heartbeat. Validations were conducted on a mock circulatory system, and the heartbeat component was extracted well by the proposed method. Also, heartbeat synchronization control was achieved without any additional sensors in the test environment.

Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image.

BACKGROUND: The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining. Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection. RESULTS: A new descriptor referred to as Segmental HOG was developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections. The new descriptor possesses flexible blocks that can be adaptively fitted to input images in order to acquire robustness for the detection of the glomeruli. Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution. Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG. CONCLUSION: The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

Automated image analysis of a glomerular injury marker desmin in spontaneously diabetic Torii rats treated with losartan.

Diabetic nephropathy is a major complication in diabetes and a leading cause of end-stage renal failure. Glomerular podocytes are functionally and structurally injured early in diabetic nephropathy. A non-obese type 2 diabetes model, the spontaneously diabetic Torii (SDT) rat, is of increasing preclinical interest because of its pathophysiological similarities to human type 2 diabetic complications including diabetic nephropathy. However, podocyte injury in SDT rat glomeruli and the effect of angiotensin II receptor blocker treatment in the early stage have not been reported in detail. Therefore, we have evaluated early stages of glomerular podocyte damage and the beneficial effect of early treatment with losartan in SDT rats using desmin as a sensitive podocyte injury marker. Moreover, we have developed an automated, computational glomerulus recognition method and illustrated its specific application for quantitatively studying glomerular desmin immunoreactivity. This state-of-the-art method enabled automatic recognition and quantification of glomerular desmin-positive areas, eliminating the need to laboriously trace glomerulus borders by hand. The image analysis method not only enabled assessment of a large number of glomeruli, but also clearly demonstrated that glomerular injury was more severe in the juxtamedullary region than in the superficial cortex region. This applied not only in SDT rat diabetic nephropathy but also in puromycin aminonucleoside-induced nephropathy, which was also studied. The proposed glomerulus image analysis method combined with desmin immunohistochemistry should facilitate evaluations in preclinical drug efficacy studies as well as elucidation of the pathophysiology of diabetic nephropathy.