Commuting Toeplitz operators on the bidisk

AbstractA necessary and sufficient condition is obtained for two Toeplitz operators to be commuting on the Hardy space of the bidisk. The main tool is the Berezin transform and the harmonic extension

PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage

Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH) typically presents acutely, prompt and expedited radiological examination is crucial for diagnosis, localization, and quantification of the hemorrhage. Early detection and accurate segmentation of perihematomal edema (PHE) play a critical role in guiding appropriate clinical intervention and enhancing patient prognosis. However, the progress and assessment of computer-aided diagnostic methods for PHE segmentation and detection face challenges due to the scarcity of publicly accessible brain CT image datasets. This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage. The dataset comprises 120 brain CT scans and 7,022 CT images, along with corresponding medical information of the patients. To demonstrate its effectiveness, classical algorithms for semantic segmentation, object detection, and radiomic feature extraction are evaluated. The experimental results confirm the suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation, detection and radiomic feature extraction methods. To the best of our knowledge, this is the first publicly available dataset for PHE in SICH, comprising various data formats suitable for applications across diverse medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to explore novel algorithms, providing valuable support for clinicians and patients in the clinical setting. PHE-SICH-CT-IDS is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937

Percutaneous nephrolithotripsy: C-arm CT with 3D virtual navigation in non-dilated renal collecting systems

PURPOSE:We aimed to evaluate the clinical superiority of using C-arm computed tomography (CT) to establish percutaneous nephrolithotripsy (PCNL) access for patients with non-dilated renal collecting systems.METHODS:From May 2014 to May 2015, 33 patients underwent C-arm CT-guided puncture to establish PCNL access after failed attempts of ultrasonography-guided nephrostomy. Technical success, procedure details, radiation exposure, complications, and stone-free rate were recorded.RESULTS:The technical success rate was 97% (32/33) with a mean puncture score of 4.5/5. Mean puncture, dilation, and fragmentation times were 17.9±6.0, 12.6±3.9, and 33.1±8.8 minutes, respectively. Mean radiation exposure was 4.8±2.1 mSv without serious complications. Stone-free rate was 93.8%.CONCLUSION:C-arm CT is a useful tool to establish PCNL access, particularly in cases of upper pole access or complicated anatomy

ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions

Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis, as well as reducing the workload of doctors. However, the absence of publicly available endometrial cancer image datasets restricts the application of computer-assisted diagnostic techniques.In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with a total of 7159 images in multiple formats. In order to prove the effectiveness of segmentation methods on ECPC-IDS, five classical deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with a total of 3579 images and XML files with annotation information. Six deep learning methods are selected for experiments on the detection task.This study conduct extensive experiments using deep learning-based semantic segmentation and object detection methods to demonstrate the differences between various methods on ECPC-IDS. As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multiple images, including a large amount of information required for image and target detection. ECPC-IDS can aid researchers in exploring new algorithms to enhance computer-assisted technology, benefiting both clinical doctors and patients greatly.Comment: 14 pages,6 figure

AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation

Methods: In this study, a benchmark \emph{Abdominal Adipose Tissue CT Image Dataset} (AATTCT-IDS) containing 300 subjects is prepared and published. AATTCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3,213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATTCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks. Results: In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATTCT-IDS reveals three adipose distributions in the subject population. Conclusion: AATTCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: \url{https://figshare.com/articles/dataset/AATTCT-IDS/23807256}.Comment: 17 pages, 7 figure

Large-scale prediction of long non-coding RNA functions in a coding–non-coding gene co-expression network

Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding–non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine)

3D-printed integrative probeheads for magnetic resonance

射频探头前端作为核磁共振设备的核心部件之一，极大程度的决定着系统实验性能的优劣。探头前端通常由射频线圈、射频电路及样品检测管道等部分组成。现有的射频线圈制作技术主要是通过手工或机械手段按照所需的线圈形状进行绕制。但是，当线圈结构较为复杂、不规则，或体积尺寸较小时，常规绕制方法便难以满足结构设计和制造的精度需求，因此造成线圈性能的劣化，增大检测区域的射频场不均匀性，对核磁共振检测产生负面影响。本研究中，利用3D打印熔融沉积制造或光敏树脂选择性固化技术精确加工出一体化磁共振探头前端，使用常温液态金属填充线圈模型管路形成射频线圈，搭建出稳定的一体化磁共振射频探头。利用高精度3D打印和液态金属灌注技术制备出包含有射频线圈和定制化样品管道结构在内的一体化磁共振射频探头前端，克服了传统磁共振三维微型线圈成型困难、与样品腔匹配程度差等问题，提高了探头的信噪比，为定制化的磁共振检测提供了新思路。 该工作由厦门大学电子科学与技术学院陈忠教授、游学秋副研究员和孙惠军高级工程师共同指导完成，博士研究生谢君尧为论文第一作者。厦门大学电子科学与技术学院黄玉清高级工程师、王忻昌副教授、倪祖荣助理教授、硕士研究生张德超，化学化工学院杨朝勇教授、博士研究生李星锐，萨本栋微米纳米科学技术研究院陈宏教授为合作作者。【Abstract】Magnetic resonance (MR) technology has been widely employed in scientific research, clinical diagnosis and geological survey. However, the fabrication of MR radio frequency probeheads still face difficulties in integration, customization and miniaturization. Here, we utilized 3D printing and liquid metal filling techniques to fabricate integrative radio frequency probeheads for MR experiments. The 3D-printed probehead with micrometer precision generally consists of liquid metal coils, customized sample chambers and radio frequency circuit interfaces. We screened different 3D printing materials and optimized the liquid metals by incorporating metal microparticles. The 3D-printed probeheads are capable of performing both routine and nonconventional MR experiments, including in situ electrochemical analysis, in situ reaction monitoring with continues-flow paramagnetic particles and ions separation, and small-sample MR imaging. Due to the flexibility and accuracy of 3D printing techniques, we can accurately obtain complicated coil geometries at the micrometer scale, shortening the fabrication timescale and extending the application scenarios.The work is supported by the National Natural Science Foundation of China (Grants U1632274, 11761141010, U1805261, 11475142, 22073078, and 61801411), and China Postdoctoral Science Foundation (2017M622075).研究工作得到国家自然科学基金、中国博士后科学基金等项目支持