Volumetric breast density measurement for personalized screening: accuracy, reproducibility, consistency, and agreement with visual assessment [0.03%]
体积型乳腺密度测量的准确性、重复性及与视学评估的一致性研究
Andreas Fieselmann,Daniel Förnvik,Hannie Förnvik et al.
Andreas Fieselmann et al.
Assessment of breast density at the point of mammographic examination could lead to optimized breast cancer screening pathways. The onsite breast density information may offer guidance of when to recommend supplemental imaging for women in ...
Prediction of reader estimates of mammographic density using convolutional neural networks [0.03%]
基于卷积神经网络的乳腺密度预测方法研究
Georgia V Ionescu,Martin Fergie,Michael Berks et al.
Georgia V Ionescu et al.
Mammographic density is an important risk factor for breast cancer. In recent research, percentage density assessed visually using visual analogue scales (VAS) showed stronger risk prediction than existing automated density measures, sugges...
Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation [0.03%]
基于核相似度测度的自适应贝叶斯标签融合海马分割方法
David Cárdenas-Peña,Andres Tobar-Rodríguez,German Castellanos-Dominguez et al.
David Cárdenas-Peña et al.
The effectiveness of brain magnetic resonance imaging (MRI) as a useful evaluation tool strongly depends on the performed segmentation of associated tissues or anatomical structures. We introduce an enhanced brain segmentation approach of B...
Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue [0.03%]
卵巢组织光学相干断层扫描图像分割算法的评估
Travis W Sawyer,Photini F S Rice,David M Sawyer et al.
Travis W Sawyer et al.
Ovarian cancer has the lowest survival rate among all gynecologic cancers predominantly due to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniqu...
Impact of prevalence and case distribution in lab-based diagnostic imaging studies [0.03%]
基于实验室的诊断影像研究中患病率和病例分布的影响
Brandon D Gallas,Weijie Chen,Elodia Cole et al.
Brandon D Gallas et al.
We investigated effects of prevalence and case distribution on radiologist diagnostic performance as measured by area under the receiver operating characteristic curve (AUC) and sensitivity-specificity in lab-based reader studies evaluating...
Sean D Rose,Emil Y Sidky,Ingrid Reiser et al.
Sean D Rose et al.
Fiber-like features are an important aspect of breast imaging. Vessels and ducts are present in all breast images, and spiculations radiating from a mass can indicate malignancy. Accordingly, fiber objects are one of the three types of sign...
Catheter segmentation in three-dimensional ultrasound images by feature fusion and model fitting [0.03%]
基于特征融合与模型拟合的导管三維超声图像分割方法
Hongxu Yang,Caifeng Shan,Arash Pourtaherian et al.
Hongxu Yang et al.
Ultrasound (US) has been increasingly used during interventions, such as cardiac catheterization. To accurately identify the catheter inside US images, extra training for physicians and sonographers is needed. As a consequence, automated se...
Roger Trullo,Caroline Petitjean,Bernard Dubray et al.
Roger Trullo et al.
Segmentation of organs at risk (OAR) in computed tomography (CT) is of vital importance in radiotherapy treatment. This task is time consuming and for some organs, it is very challenging due to low-intensity contrast in CT. We propose a fra...
Comparison of screening full-field digital mammography and digital breast tomosynthesis technical recalls [0.03%]
数字化乳腺全视野摄影和断层合成技术召回情况的比较分析
Lonie R Salkowski,Mai Elezaby,Amy M Fowler et al.
Lonie R Salkowski et al.
Enhancing quality using the inspection program (EQUIP) augments the FDA/MQSA program ensuring image quality review and implementation of corrective processes. We compared technical recalls between digital breast tomosynthesis (DBT) and full...
Special Section Guest Editorial: Artificial Intelligence in Medical Imaging [0.03%]
医学影像人工智能特邀专栏編委會公告
Elizabeth A Krupinski,Paul Kinahan,Patrick La Riviere
Elizabeth A Krupinski
This editorial provides an overview of the articles in the special section.