Cortical sinus probing, S1P1-dependent entry and flow-based capture of egressing T cells.

The cellular dynamics of the egress of lymphocytes from lymph nodes are poorly defined. Here we visualized the branched organization of lymph node cortical sinuses and found that after entry, some T cells were retained, whereas others returned to the parenchyma. T cells deficient in sphingosine 1-phosphate receptor type 1 probed the sinus surface but failed to enter the sinuses. In some sinuses, T cells became rounded and moved unidirectionally. T cells traveled from cortical sinuses into macrophage-rich sinus areas. Many T cells flowed from medullary sinuses into the subcapsular space. We propose a multistep model of lymph node egress in which cortical sinus probing is followed by entry dependent on sphingosine 1-phosphate receptor type 1, capture of cells in a sinus region with flow, and transport to medullary sinuses and the efferent lymph

Effciently Compressing 3D Medical Images for Teleinterventions via CNNs and Anisotropic Diffusion.

PURPOSE: Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ-specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter. METHODS: The proposed method, DLAD, uses a CNN architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC-visually lossless, is applied to compress the image. We demonstrate the proposed method on 3D CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak-signal-to-noise ratio (PSNR), structural similarity (SSIM) and compression ratio (CR) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images. RESULTS: The results show that the method can significantly improve CR of most well-known compression methods. DLAD combined with HEVC-visually lossless achieves the highest average CR of 6.45, which is 36% higher than that of the original HEVC and out-performs other state-of-the-art lossless medical image compression methods. The means of PSNR and SSIM are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation. CONCLUSIONS: We thus conclude that the method has a high potential to be applied in teleintervention applications