Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model [0.03%]
学习用一个通用模型在X光图像中定位跨解剖标志点
Heqin Zhu,Qingsong Yao,Li Xiao et al.
Heqin Zhu et al.
Objective and Impact Statement. In this work, we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical regions. Compared with the conventional model trained on...
Three-Dimensional Shear Wave Elastography Using a 2D Row Column Addressing (RCA) Array [0.03%]
利用二维行列寻址(RCA)阵列的三维剪切波弹性成像技术
Zhijie Dong,Jihun Kim,Chengwu Huang et al.
Zhijie Dong et al.
Objective. To develop a 3D shear wave elastography (SWE) technique using a 2D row column addressing (RCA) array, with either external vibration or acoustic radiation force (ARF) as the shear wave source. Impact Statement. The proposed metho...
Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus [0.03%]
正常压力性脑积水的自动分割和连通性分析方法研究
Angela Zhang,Amil Khan,Saisidharth Majeti et al.
Angela Zhang et al.
Objective and Impact Statement. We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with M...
High-Resolution Multiscale Imaging Enabled by Hybrid Open-Top Light-Sheet Microscopy [0.03%]
混合型开放式照明片层显微技术实现高分辨率多尺度成像
Hong Ye,Guohua Shi
Hong Ye
Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation [0.03%]
联合优化空间直方图UNET架构（JOSHUA）在脂肪组织分割中的应用
Joshua K Peeples,Julie F Jameson,Nisha M Kotta et al.
Joshua K Peeples et al.
Objective. We aim to develop a machine learning algorithm to quantify adipose tissue deposition at surgical sites as a function of biomaterial implantation. Impact Statement. To our knowledge, this study is the first investigation to apply ...
Particle-Mediated Histotripsy for the Targeted Treatment of Intraluminal Biofilms in Catheter-Based Medical Devices [0.03%]
粒子介导的组织消融术在导管相关医疗器械内腔生物膜精准治疗中的应用研究
Christopher Childers,Connor Edsall,Isabelle Mehochko et al.
Christopher Childers et al.
Objective. This paper is an initial work towards developing particle-mediated histotripsy (PMH) as a novel method of treating catheter-based medical device (CBMD) intraluminal biofilms. Impact Statement. CBMDs commonly become infected with ...
Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma [0.03%]
基于深度分割特征的影像组学改善肝细胞癌复发预测能力
Jifei Wang,Dasheng Wu,Meili Sun et al.
Jifei Wang et al.
Objective and Impact Statement. This study developed and validated a deep semantic segmentation feature-based radiomics (DSFR) model based on preoperative contrast-enhanced computed tomography (CECT) combined with clinical information to pr...
Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning [0.03%]
深度学习在结直肠癌淋巴结转移预测中的应用研究
Hailing Liu,Yu Zhao,Fan Yang et al.
Hailing Liu et al.
Objective. To develop an artificial intelligence method predicting lymph node metastasis (LNM) for patients with colorectal cancer (CRC). Impact Statement. A novel interpretable multimodal AI-based method to predict LNM for CRC patients by ...
Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution [0.03%]
基于对比学习的空间图卷积连接皮层分割
Peiting You,Xiang Li,Fan Zhang et al.
Peiting You et al.
Objective. Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity. Impact Statement. The proposed framework utilizes novel spatial-graph repr...
Endoscopic Coregistered Ultrasound Imaging and Precision Histotripsy: Initial In Vivo Evaluation [0.03%]
内窥镜联合超声成像和精密聚焦史脱术的体内实验初步研究
Thomas G Landry,Jessica Gannon,Eli Vlaisavljevich et al.
Thomas G Landry et al.
Objective. Initial performance evaluation of a system for simultaneous high-resolution ultrasound imaging and focused mechanical submillimeter histotripsy ablation in rat brains. Impact Statement. This study used a novel combination of high...