Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images

This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.Comment: This paper was published and presented in SPIE Medical Imaging 201

Comparison of retinal thickness measurements of normal eyes between topcon algorithm and a graph based algorithm

To assess the agreement between Topcon built-in algorithm and our developed graph based algorithm, the retinal thickness of 9-sectors on an Early Treatment of Diabetic Retinopathy Study(ETDRS) chart measurements for normal subjects was compared. A total of fifty eyes were enrolled in this study. The overall and sectoral thickness on ETDRS chart were calculated using Topcon built-in algorithm and our developed three-dimensional graph based algorithm. Correlation analysis and agreement analysis were performed between the commercial algorithm and our algorithm. A high degree of correlation was found between the results obtained from the two methods was from 0.856 to 0.960. It’s showed that our developed graph based algorithm can provide excellent performance similar to Topcon algorithm

PPT: Token Pruning and Pooling for Efficient Vision Transformers

Vision Transformers (ViTs) have emerged as powerful models in the field of computer vision, delivering superior performance across various vision tasks. However, the high computational complexity poses a significant barrier to their practical applications in real-world scenarios. Motivated by the fact that not all tokens contribute equally to the final predictions and fewer tokens bring less computational cost, reducing redundant tokens has become a prevailing paradigm for accelerating vision transformers. However, we argue that it is not optimal to either only reduce inattentive redundancy by token pruning, or only reduce duplicative redundancy by token merging. To this end, in this paper we propose a novel acceleration framework, namely token Pruning & Pooling Transformers (PPT), to adaptively tackle these two types of redundancy in different layers. By heuristically integrating both token pruning and token pooling techniques in ViTs without additional trainable parameters, PPT effectively reduces the model complexity while maintaining its predictive accuracy. For example, PPT reduces over 37% FLOPs and improves the throughput by over 45% for DeiT-S without any accuracy drop on the ImageNet dataset. The code is available at https://github.com/xjwu1024/PPT and https://github.com/mindspore-lab/models

Differential miRNA expression in Rehmannia glutinosa plants subjected to continuous cropping

<p>Abstract</p> <p>Background</p> <p>The productivity of the medicinally significant perennial herb <it>Rehmannia glutinosa </it>is severely affected after the first year of cropping. While there is some information available describing the physiological and environmental causes of this yield decline, there is as yet no data regarding the changes in gene expression which occur when the species is continuously cropped.</p> <p>Results</p> <p>Using a massively parallel (Solexa) DNA sequencing platform, it was possible to identify and quantify the abundance of a large number of <it>R. glutinosa </it>miRNAs. We contrasted the miRNA content of first year crop plants with that of second year crop ones, and were able to show that of 89 conserved (belonging to 25 families) and six novel miRNAs (six families), 29 of the former and three of the latter were differentially expressed. The three novel miRNAs were predicted to target seven genes, and the 29 conserved ones 308 genes. The potential targets of 32 of these differentially expressed miRNAs involved in the main transcription regulation, plant development and signal transduction. A functional analysis of the differentially expressed miRNAs suggested that several of the proposed targets could be directly or indirectly responsible for the development of the tuberous root.</p> <p>Conclusion</p> <p>We have compared differential miRNAs expression in the first year crop (FP) <it>R. glutinosa </it>plants and second year crop (SP) ones. The outcome identifies some potential leads for understanding the molecular basis of the processes underlying the difficulty of maintaining the productivity of continuously cropped <it>R. glutinosa</it>.</p

Effectiveness of hydropathic compress of dandelion in ameliorating complications of arteriovenous fistula

Purpose: To investigate the effect of dandelion hydropathic compress on the complications of autologous arteriovenous fistula (AVF).Methods: From January to June 2019, a total of 162 patients treated with arteriovenous fistula for hemodialysis in the blood purification department of Affiliated Hospital of Jining Medical University were enrolled. They were randomly assigned at a ratio of 1:1 to receive either conventional infrared irradiation (control group) or conventional irradiation plus dandelion hydropathic compress (study group). The clinical endpoint was the amelioration of the complications of arteriovenous fistula after 6 months of treatment.Results: Dandelion hydropathic compress combined with conventional infrared irradiation was associated with a significantly higher clinical efficacy (96.30 %) than conventional infrared irradiation alone (77.78 %). The application of dandelion hydropathic compress plus infrared irradiation resulted in significantly reduced pain, a better quality of life, and a lower incidence of complications (p &lt; 0.05).Conclusion: Dandelion hydropathic compress plus routine nursing and infrared irradiation lower the incidence of complications, improve blood flow, relieve pain, and enhance the quality of life of patients. Further clinical trials are needed to confirm the usefulness of this therapeutic strategy

An automated framework of inner segment/outer segment defect detection for retinal SD-OCT images

The integrity of inner segment/outer segment (IS/OS) has high correlation with lower visual acuity in patients suffering from blunt trauma. An automated 3D IS/OS defect detection method based on the SD-OCT images was proposed. First, 11 surfaces were automatically segmented using the multiscale 3D graph-search approach. Second, the sub-volumes between surface 7 and 8 containing IS/OS region around the fovea (diameter of mm) were extracted and flattened based on the segmented retinal pigment epithelium layer. Third, 5 kinds of texture based features were extracted for each voxel. A KNN classifier was trained and each voxel was classified as disrupted or nondisrupted and the responding defect volume was calculated. The proposed method was trained and tested on 9 eyes from 9 trauma subjects using the leave-one-out cross validation method. The preliminary results demonstrated the feasibility and efficiency of the proposed method

Extended Wiener-Khinchin theorem for quantum spectral analysis

The classical Wiener-Khinchin theorem (WKT), which can extract spectral information by classical interferometers through Fourier transform, is a fundamental theorem used in many disciplines. However, there is still need for a quantum version of WKT, which could connect correlated biphoton spectral information by quantum interferometers. Here, we extend the classical WKT to its quantum counterpart, i.e., extended WKT (e-WKT), which is based on two-photon quantum interferometry. According to the e-WKT, the difference-frequency distribution of the biphoton wavefunctions can be extracted by applying a Fourier transform on the time-domain Hong-Ou-Mandel interference (HOMI) patterns, while the sum-frequency distribution can be extracted by applying a Fourier transform on the time-domain NOON state interference (NOONI) patterns. We also experimentally verified the WKT and e-WKT in a Mach-Zehnder interference (MZI), a HOMI and a NOONI. This theorem can be directly applied to quantum spectroscopy, where the spectral correlation information of biphotons can be obtained from time-domain quantum interferences by Fourier transform. This may open a new pathway for the study of light-matter interaction at the single photon level.Comment: 13 pages, 5 figure