The blurriness of boundaries in medical image target regions hinders further improvement in automatic segmentation accuracy and is a challenging problem. To address this issue, we propose a model called long-distance perceptual UNet (LD-UNet), which has a powerful long-‍distance perception ability and can effectively perceive the semantic context of an entire image. Specifically, LD-UNet utilizes global and local long-distance induction modules, which endow the model with contextual semantic induction capabilities for long-distance feature dependencies. The modules perform long-distance semantic perception at the high and low stages of LD-UNet, respectively, effectively improving the accuracy of local blurred information assessment. We also propose a top-down deep supervision method to enhance the ability of the model to fit data. Then, extensive experiments on four types of tumor data with blurred boundaries are conducted. The dataset includes nasopharyngeal carcinoma, esophageal carcinoma, pancreatic carcinoma, and colorectal carcinoma. The dice similarity coefficient scores obtained by LD-UNet on the four datasets are 73.35%, 85.93%, 70.04%, and 82.71%. Experimental results demonstrate that LD-UNet is more effective in improving the segmentation accuracy of blurred boundary regions than other methods with long-distance perception, such as transformers. Among all models, LD-UNet achieves the best performance. By visualizing the feature dependency field of the models, we further explore the advantages of LD-UNet in segmenting blurred boundaries.

This study utilizes radiomics to explore imaging biomarkers for predicting the recurrence of chronic subdural hematoma (CSDH), aiming to improve the prediction of CSDH recurrence risk. Analyzing CT scans from 64 patients with CSDH, we extracted 107 radiomic features and employed recursive feature elimination (RFE) and the XGBoost algorithm for feature selection and model construction. The feature selection process identified six key imaging biomarkers closely associated with CSDH recurrence: flatness, surface area to volume ratio, energy, run entropy, small area emphasis, and maximum axial diameter. The selection of these imaging biomarkers was based on their significance in predicting CSDH recurrence, revealing deep connections between postoperative variables and recurrence. After feature selection, there was a significant improvement in model performance. The XGBoost model demonstrated the best classification performance, with the average accuracy improving from 46.82

Abstract Analysis of the pathology image is important to diagnose cancer of lung, breast and stomach. Segmenting the nucleus is a key step for quantitative analysis, and has significance to the pathology researches and computer aided diagnosis systems. The inconsistency of colour, fuzzy boundary of nucleus and overlapping of cells are the universally acknowledged challenges. To solve these problems, the difference between the inside and outside of nucleus is enhanced by obtaining the distribution of the haematoxylin based on Lambert–Beer's law and the optical characteristics of stains. An inferior encoder, which is supervised by the inferior decoder, is proposed to extract the deep features of the distribution of stains. And these features are fed into the primary encoder to improve the accuracy of segmentation. To relieve the problem that some nuclei are segmented as background because the deep feature is inapparent, dynamic convolution is introduced into the encoders. The experiments show that the proposed model can segment the nucleus in the pathological images more precisely than the compared models. The Dice similarity coefficient (DSC) and panoptic quality (PQ) are 0.810 and 0.512, respectively.

BACKGROUND:The number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting. METHODS:We retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard). RESULTS:In the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1-4, 5-9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort. CONCLUSIONS:The AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection.

Background Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors. Purpose To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery. Material and Methods In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts. Results Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer. Conclusion The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.

Introduction Automatically and accurately delineating the primary nasopharyngeal carcinoma (NPC) tumors in head magnetic resonance imaging (MRI) images is crucial for patient staging and radiotherapy. Inspired by the bilateral symmetry of head and complementary information of different modalities, a multi-modal neural network named BSMM-Net is proposed for NPC segmentation. Methods First, a bilaterally symmetrical patch block (BSP) is used to crop the image and the bilaterally flipped image into patches. BSP can improve the precision of locating NPC lesions and is a simulation of radiologist locating the tumors with the bilateral difference of head in clinical practice. Second, modality-specific and multi-modal fusion features (MSMFFs) are extracted by the proposed MSMFF encoder to fully utilize the complementary information of T1- and T2-weighted MRI. The MSMFFs are then fed into the base decoder to aggregate representative features and precisely delineate the NPC. MSMFF is the output of MSMFF encoder blocks, which consist of six modality-specific networks and one multi-modal fusion network. Except T1 and T2, the other four modalities are generated from T1 and T2 by the BSP and DT modal generate block. Third, the MSMFF decoder with similar structure to the MSMFF encoder is deployed to supervise the encoder during training and assure the validity of the MSMFF from the encoder. Finally, experiments are conducted on the dataset of 7633 samples collected from 745 patients. Results and discussion The global DICE, precision, recall and IoU of the testing set are 0.82, 0.82, 0.86, and 0.72, respectively. The results show that the proposed model is better than the other state-of-the-art methods for NPC segmentation. In clinical diagnosis, the BSMM-Net can give precise delineation of NPC, which can be used to schedule the radiotherapy.

Objective This study aims to differentiate preoperative Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL) by transfer learning radiomics nomogram (TLRN) with whole slide images of GC as source domain data. Materials and Methods This study retrospectively enrolled 438 patients with histopathologic diagnoses of Borrmann type IV GC and PGL. They received CT examinations from three hospitals. Quantitative transfer learning features were extracted by the proposed transfer learning radiopathomic network and used to construct transfer learning radiomics signatures (TLRS). A TLRN, which integrates TLRS, clinical factors, and CT subjective findings, was developed by multivariate logistic regression. The diagnostic TLRN performance was assessed by clinical usefulness in the independent validation set. Results The TLRN was built by TLRS and a high enhanced serosa sign, which showed good agreement by the calibration curve. The TLRN performance was superior to the clinical model and TLRS. Its areas under the curve (AUC) were 0.958 (95% confidence interval [CI], 0.883–0.991), 0.867 (95% CI, 0.794–0.922), and 0.921 (95% CI, 0.860–0.960) in the internal and two external validation cohorts, respectively. Decision curve analysis (DCA) showed that the TLRN was better than any other model. TLRN has potential generalization ability, as shown in the stratification analysis. Conclusions The proposed TLRN based on gastric WSIs may help preoperatively differentiate PGL from Borrmann type IV GC. Borrmann type IV gastric cancer, primary gastric lymphoma, transfer learning, whole slide image, deep learning.

ObjectiveTo compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC).MethodsBetween November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort. Based on AB-MRI, a TL signature (TLS) and a radiomics signature (RS) were built with the least absolute shrinkage and selection operator (LASSO) logistic regression. Their diagnostic performances were validated and compared using areas under the receiver operating curve (AUCs), net reclassification improvement (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and stratification analysis. A convolutional filter visualization technique was used to map the response areas of LVI on the AB-MRI.ResultsIn the validation cohort, compared with RS, the TLS showed better capability in discriminating LVI-positive from LVI-negative lesions (AUC: 0.852 vs. 0.726, p < 0.001; IDI = 0.092, p < 0.001; NRI = 0.554, p < 0.001). The diagnostic performance of TLS was not affected by the menstrual state, molecular subtype, or contrast agent type (all p > 0.05). Moreover, DCA showed that the TLS added more net benefit than RS for clinical utility.ConclusionsAn AB-MRI-based TLS was superior to RS for preoperative LVI prediction in patients with clinically node-negative IBC.

针对胸腺瘤患者术前危险程度的预测问题,提出了结合深度学习与改进的极限学习机的集成学习计算机辅助分析方法.首先,将胸腺瘤CT图像通过小波多尺度变换到不同的尺度下并计算小波能量图,以增加图像信息的丰富性和多样性;其次,利用小波能量图训练卷积神经网络模型,并利用卷积核提取小波能量图中与任务相关的特异性深度特征;最后,基于改进的极限学习机为基分类器训练具有差异性的子模型并构建集成学习分类模型,以提高模型的稳定性和预测精度.多中心实验结果表明,所提方法有较好的泛化性能和稳定性,3个验证集的AUC分别为0.833,0.771,0.784.

Abstract Due to the low contrast, blurred boundary and intensity inhomogeneity of the images, accurate segmentation of breast cancer lesions with dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) still has great challenges. This paper proposed an improved active contour model (ACM) for segmenting breast cancer lesions in DCE‐MRI images. First, based on the extreme learning machine (ELM) method, a robust function is proposed that combines image intensities and time‐domain features to enhance the difference between the lesions and other tissues. Second, an edge‐stop function (ESF) is introduced by combining the image intensity, time‐domain feature, and Hessian shape index to detect the irregular and blurred boundaries. At the boundary of breast cancer lesions, the energy function of ACM is minimized and the evolution of the contour curve completes, so the accurate lesion region of breast cancer can be segmented. The mean Dice similar coefficient (DICE), Jaccard similarity (JC) and Hausdorff distance (HD) of the segmentation of the proposed model in 50 samples are 85.88±6.62%, 75.72±9.68% and 11.62±4.72 mm, respectively. The results segmented by the proposed ACM are more similar to the manual segmentation than the compared models.

To solve the tow contrast, blurred boundry and intensity inhomogeous of the breast cancer lesions in the dynamic contrast-enhanced magnetic resonance imaging images, an integrated active contour model is proposed by combining markov random field energy with time-domain features. First, the edge-stop function of active contour model is derived from a fuzzy c-means cluster which treat the intensity and variation of time-domain as the feature. Then, markov random field energy is constructed to improve the difference between the lesions and other tissues. Finally, the region term is derived from k-nearest neighbor method which treat markov random field energy as dataset. The evolution of the contour curve stops at the boundary of lesions, and the energy function constructed by region term and edge term is minimized.The experiment proved that markov random field energy and time-domain feature can improve the contrast between the breast tumours and other tissues. Compared with state of the art of active contours models, the result segmented by the proposed method is more similar to the artificial segmentation, so that the proposed method is meaningful for breast cancer segmentation.

当前，我国经济快速发展，科技水平显著提高，产业结构逐步调整，推动社会对高技能、高素质、应用型人才需求越来越多，使人才供给与需求关系发生了深刻变化。人才的培养是国家经济发展、科技进步的关键。地方高等院校担任着向应用型转型发展的任务，急需向社会输送满足要求的应用型人才。这需要应用型本科院校要充分认识人才培养面临的问题及其产生的原因，找准学校在人才培养方面的定位，探索出适合自身发展的应用型人才培养对策。

为了预测并确认结构设计阶段快速反射镜系统的频率特性和时域性能,对一种两轴柔性支承快速反射镜进行了结构控制一体化研究和测试。提出了一种新型柔性支承结构,根据激光系统光束传输要求的快速反射镜指标,设计了快速反射镜系统的主要结构参数;研究了系统的动态数学模型,确定了闭环系统的控制方式和控制参数;建立了系统摆动部分的刚柔耦合模型,获得了结构非线性模型,基于非线性模型对控制系统和运动系统进行了联合仿真测试。联合仿真结果显示,系统在运动方向的谐振频率为54 Hz,与有限元和理论计算结果的误差均为3.8%,系统位置闭环带宽为203 Hz,符合设计要求。时域输出结果显示,系统的超调量为3.5%,调节时间为10 ms,与理论计算结果的偏差分别为3%和5 ms。

微机原理是自动化专业的一门多学科交叉且理论性和实践性都较强的专业基础课;针对微机原理课程中计算机的概念性结构与功能特性复杂、抽象,且通常不与某一实际硬件相挂钩的特点;采用模块化教学理念对理论体系及实践体系进行构建,使学生掌握微机原理的基本知识和技能,通过项目驱动,完善学生的微机原理和专业知识体系,提高学生的综合设计能力.

为研究高速电梯轿厢内声场特性及其噪声主动控制系统性能,根据计算流体力学、声学有限元法、声振耦合理论,获取高速电梯在井道内上行时的流场分布规律,并将电梯轿厢外表面的气体脉动压力作为电梯轿厢内噪声的激励,建立对应的声振耦合计算模型,揭示高速电梯轿厢外空气脉动规律及轿厢内噪声的传播特性.并基于主动噪声控制的声学势能最小准则,揭示7种次级声源不同的噪声主动控制系统的降噪性能,可为高速电梯轿厢内噪声控制优化提供指导.