On a relaxation-labelling algorithm for quantitative assessment of tumour vasculature in tissue section images

Although tumour vasculature constitutes a biological factor playing a crucial role in the radiation response of tumours, the current procedures of assessment are semiquantitative, typically employing visual examination of stained histological material. Such techniques are also time consuming, and inefficient of extracting essential information on the vascular network. Image analysis has yet to contribute significantly in this direction, and most studies to date focus on blood vessel segmentation through empirical, user-selected thresholds. The present paper proposes an alternative segmentation approach, based on a probabilistic relaxation algorithm, applied in microscopic images of stained tissues. After image partitioning various information is obtained, such as vascular domains and geometrical characteristics of vessels.     

基于几何活动轮廓的神经元干细胞序列图像的自动分割

一个快速稳定的分割系统是研究神经元干细胞变化的基础，为完善此系统，针对多连接边缘模糊的细胞分割提取问题，根据曲线进化原理，我们提出了一种基于水平集方法的改进的几何活动轮廓算法。此算法能自动解决图像的拓扑变化，并能获得更加真实的细胞轮廓边缘。将此方法应用于神经元干细胞的序列图像分割，实验结果证明了此算法的有效性与准确性。     

Methods to address metal artifacts in post-processed CT images – A do-it-yourself guide for orthopedic surgeons

Computed tomography (CT) scans are often used for postoperative imaging in orthopedics. In the presence of metallic hardware, artifacts are generated, which can hamper visualization of the CT images, and also render the study ineffective for 3-D printing. Various solutions are available to minimize metal artifacts, and radiologists can employ these before or after processing the CT study. However, the orthopedic surgeon may be faced with situations where the metal artifacts were not addressed. To counter such problems, we present three do-it-yourself (DIY) techniques that can be used to manage metal artifacts.     

基于数字乳腺图像分割算法的比较研究

乳腺疾病的发病率近年来不断增高,为增加诊断乳腺疾病的正确率和减轻医生的诊断负担,计算机辅助诊断技术应运而生。本文主要介绍运用阈值法和边缘检测法分割数字乳腺图像,实现对乳腺钙化点边缘的检测。     

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

We propose a joint segmentation and groupwise registration method for dynamic cardiac perfusion images that uses temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal while segmentation minimizes a pixel’s dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared with two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutual complementary registration and segmentation information in one framework while other methods solve the two problems separately.     

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a “context penalty” that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method’s robustness to noise and inaccurate segmentations.