[White blood segmentation based on dual path and atrous spatial pyramid pooling].

The count and recognition of white blood cells in blood smear images play an important role in the diagnosis of blood diseases including leukemia. Traditional manual test results are easily disturbed by many factors. It is necessary to develop an automatic leukocyte analysis system to provide doctors with auxiliary diagnosis, and blood leukocyte segmentation is the basis of automatic analysis. In this paper, we improved the U-Net model and proposed a segmentation algorithm of leukocyte image based on dual path and atrous spatial pyramid pooling. Firstly, the dual path network was introduced into the feature encoder to extract multi-scale leukocyte features, and the atrous spatial pyramid pooling was used to enhance the feature extraction ability of the network. Then the feature decoder composed of convolution and deconvolution was used to restore the segmented target to the original image size to realize the pixel level segmentation of blood leukocytes. Finally, qualitative and quantitative experiments were carried out on three leukocyte data sets to verify the effectiveness of the algorithm. The results showed that compared with other representative algorithms, the proposed blood leukocyte segmentation algorithm had better segmentation results, and the mIoU value could reach more than 0.97. It is hoped that the method could be conducive to the automatic auxiliary diagnosis of blood diseases in the future.

[A novel method of optic disk segmentation based on visual saliency and rotary scanning].

Fast optic disk localization and boundary segmentation is an important research topic in computer aided diagnosis. This paper proposes a novel method to effectively segment optic disk by using human visual characteristics in analyzing and processing fundus image. After a general analysis of optic disk features in fundus images, the target of interest could be located quickly, and intensity, color and spatial distribution of the disc are used to generate saliency map based on pixel distance. Then the adaptive threshold is used to segment optic disk. Moreover, to reduce the influence of vascular, a rotary scanning method is devised to achieve complete and continuous contour of optic disk boundary. Tests in the public fundus images database Drishti-GS have good performances, which mean that the proposed method is simple and rapid, and it meets the standard of the eye specialists. It is hoped that the method could be conducive to the computer aided diagnosis of eye diseases in the future.

[Comparison of heart rate variability measurements between ballistocardiogram and electrocardiography].

OBJECTIVE: To compare the heart rate variability (HRV) measurements between ballistocardiogram (BCG) and electrocardiography (ECG). METHODS: The signals of BCG and ECG of 21 patients were collected synchronously. JJ intervals of BCG and RR intervals of ECG were used to calculate the cardiac periods. The parameters of HRV analysis were calculated in time domain analysis, frequency domain analysis and nonlinear analysis. The results derived from BCG and ECG were compared. RESULTS: The parameters of HRV analysis calculated from BCG and ECG had high similarity. The correlation coefficients of SDNN, TP, LF, HF and SD2 between the BCG and ECG methods were high (r = 1). The correlation coefficients of rMSSD and SD2 were 0.99 and of PNN50 and LF/HF were 0.98 between the two methods. HRV analysis results derived from the two methods were similar (P > 0.05). CONCLUSION: HRV could also be measured reliably by calculating the JJ interval from BCG.

MCFSA-Net: A multi-scale channel fusion and spatial activation network for retinal vessel segmentation.

As the only vascular tissue that can be directly viewed in vivo, retinal vessels are medically important in assisting the diagnosis of ocular and cardiovascular diseases. They generally appear as different morphologies and uneven thickness in fundus images. Therefore, the single-scale segmentation method may fail to capture abundant morphological features, suffering from the deterioration in vessel segmentation, especially for tiny vessels. To alleviate this issue, we propose a multi-scale channel fusion and spatial activation network (MCFSA-Net) for retinal vessel segmentation with emphasis on tiny ones. Specifically, the Hybrid Convolution-DropBlock (HC-Drop) is first used to extract deep features of vessels and construct multi-scale feature maps by progressive down-sampling. Then, the Channel Cooperative Attention Fusion (CCAF) module is designed to handle different morphological vessels in a multi-scale manner. Finally, the Global Spatial Activation (GSA) module is introduced to aggregate global feature information for improving the attention on tiny vessels in the spatial domain and realizing effective segmentation for them. Experiments are carried out on three datasets including DRIVE, CHASE_DB1, and STARE. Our retinal vessel segmentation method achieves Accuracy of 96.95%, 97.57%, and 97.83%, and F1 score of 82.67%, 81.82%, and 82.95% in the above datasets, respectively. Qualitative and quantitative analysis show that the proposed method outperforms current advanced vessel segmentation methods, especially for tiny vessels.

Complete F-type mitochondrial genome of Chinese freshwater mussel Lamprotula tortuosa.

Lamprotula tortuosa is the endemic species of freshwater pearl mussel in China. The complete F-type mitochondrial genome of L. tortuosa was first determined. This circle genome (15,722 bp in size) comprises 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, except for COI, CYTB, ND6, and ND4L, with TTG, ATT, ATT, and ATA start codon, respectively; the remaining protein-coding genes initiated with the orthodox ATG start codon. There were 25 noncoding regions found throughout the mitogenome of L. tortuosa, ranging in size from 1 to 241 bp, the largest of which is between ND5 and tRNA(Gln) (241 bp), but is shorter than the control region sequences(about 280 bp) of freshwater mussels from Poland and South Korea.