Tractostorm: The what,why, and how of tractography dissection reproducibility

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called “virtual dissection.” Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC‐ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real‐life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and “virtual dissection” across various laboratories and hospitals. Intra‐rater agreement (Dice score) was approximately 0.77, while inter‐rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.     

Coronary Artery Lumen Segmentation Using Location–Adaptive Threshold in Coronary Computed Tomographic Angiography: A Proof-of-Concept

ObjectiveTo compare the lumen parameters measured by the location-adaptive threshold method (LATM), in which the inter- and intra-scan attenuation variabilities of coronary computed tomographic angiography (CCTA) were corrected, and the scan-adaptive threshold method (SATM), in which only the inter-scan variability was corrected, with the reference standard measurement by intravascular ultrasonography (IVUS).Materials and MethodsThe Hounsfield unit (HU) values of whole voxels and the centerline in each of the cross-sections of the 22 target coronary artery segments were obtained from 15 patients between March 2009 and June 2010, in addition to the corresponding voxel size. Lumen volume was calculated mathematically as the voxel volume multiplied by the number of voxels with HU within a given range, defined as the lumen for each method, and compared with the IVUS-derived reference standard. Subgroup analysis of the lumen area was performed to investigate the effect of lumen size on the studied methods. Bland-Altman plots were used to evaluate the agreement between the measurements.ResultsLumen volumes measured by SATM was significantly smaller than that measured by IVUS (mean difference, 14.6 mm³; 95% confidence interval [CI], 4.9–24.3 mm³); the lumen volumes measured by LATM and IVUS were not significantly different (mean difference, −0.7 mm³; 95% CI, −9.1–7.7 mm³). The lumen area measured by SATM was significantly smaller than that measured by LATM in the smaller lumen area group (mean of difference, 1.07 mm²; 95% CI, 0.89–1.25 mm²) but not in the larger lumen area group (mean of difference, −0.07 mm²; 95% CI, −0.22–0.08 mm²). In the smaller lumen group, the mean difference was lower in the Bland-Altman plot of IVUS and LATM (0.46 mm²; 95% CI, 0.27–0.65 mm²) than in that of IVUS and SATM (1.53 mm²; 95% CI, 1.27–1.79 mm²).ConclusionSATM underestimated the lumen parameters for computed lumen segmentation in CCTA, and this may be overcome by using LATM.     

基于自动勾画和快速蒙特卡罗计算的放射治疗全身器官剂量数据库平台的可行性研究

目的:探讨建立一种放射治疗全身器官剂量数据库平台的可行性。方法:使用基于深度学习的自动勾画软件DeepViewer?1例食管癌患者的全身CT上勾画全身器官，然后利用基于GPU加速的蒙特卡罗软件ARCHER计算相应的器官剂量分布，最后利用Lyman-Kutcher-Burman（LKB）模型评估放疗患者正常组织并发症概率（NTCP）。结果:针对该病例，成功建立基于DeepViewer?ARCHER和LKB模型的全身器官剂量数据库，发现距离靶区越近的器官剂量越大，其中心脏与靶区间距离最小，剂量为14.11 Gy，但因其模型参数特殊，通过LKB模型计算的NTCP为0.00%；左、右肺的剂量分别为3.19和1.16 Gy，但是NTCP值却很大，分别为2.13%和1.60%。对于距离靶区较远的头颈部器官（视交叉、视神经和眼）和腹部器官（直肠、膀胱和股骨头）剂量分别约为9和2 mGy，并且NTCP均近似为0.00%。结论:研究结果证明通过自动勾画软件DeepViewer?蒙特卡罗软件ARCHER和LKB模型建立全身器官剂量数据库的可行性。     

An electronic travel guide for visually impaired – vehicle board recognition system through computer vision techniques

Abstract

Purpose: In the context of assistive technology, mobility takes the meaning of “moving safely, gracefully, and comfortably”.The aim of this article is to provide a system which will be a convenient means of navigation for the Visually Impaired people, in the public transport system.

Method: A blind regular commuter who travels by public transport facility finds difficulty in identifying the vehicle that is nearing the stop. Hence, a real-time system that dynamically identifies the nearing vehicle and informs the commuters is necessary. This paper proposes such a system namely the “Vehicle Board Recognition System” (VBRS). Computer Vision techniques such as segmentation, object recognition, text detection and optical character recognition are utilized to build the system, which will detect, analyze, derive and communicate the information to the passengers.

Results: Thanks to the rapid development in technology, there are several navigation systems both hand held and wearable, available to help visually impaired (VI) people move comfortably both indoor and outdoor. Many blind people are not comfortable in using these devices or they are not affordable for them. Thus the proposed system gives them the comfort of navigation.

Conclusion: This system can be installed in the bus stop to assist the Visually Impaired, from externally rather than their hand held or wearable assistive devices.

• Implications for rehabilitation

• This proposed system will help the visually impaired to

• ensure secure navigation

• be independent of the others

• develop self confidence.

• overcome the training, affordability of wearable/ handheld devices.

     

基于机器学习的脑胶质瘤多模态影像分析

脑部胶质瘤是临床中常见的一种原发性脑肿瘤，具有复发率高、死亡率高以及治愈率低的特点。常规临床诊断主要依靠计算机断层扫描(CT)和磁共振成像(MRI)检查技术进行鉴别。随着成像技术和机器学习方法的不断发展，多模态影像智能分析技术已经逐步成为研究热点，在脑胶质瘤的病灶分割测量、肿瘤分级、预后生存周期预测和基因型辨别等方面具有重要的应用前景。本文重点介绍基于机器学习和多模态影像在脑胶质瘤临床辅助诊断和预后评估中的应用进展。     

自动痔疮套扎术(RPH)治疗轻中度痔疮

目的研究自动痔疮套扎术(RPH)治疗轻中度痔疮的临床疗效.方法应用自动痔疮套扎器对156例轻中度痔疮患者施行自动痔疮套扎术,观察手术疗效、并发症和病人满意度.结果共施行套扎治疗287例次,平均每人1.8次.术后全部患者的痔疮症状均有不同程度的好转,其中症状完全消除者143例(91.7%),以便血为主要症状者术后便血全部停止.术后主要不适有急便感或坠胀感51例(32.7%);术后痔块回缩不全8例(5.1%);无出血、感染、肛门狭窄等并发症.问卷调查病人的满意度,表示满意者149例(95.5%),基本满意6例(3.9%),不满意1例(0.6%).结论RPH对轻中度痔疮不仅具有极好的疗效,而且无需麻醉和住院,操作简便,术后痛苦轻微,几无并发症,病人满意度高.     

Effects of acute administration of d-amphetamine and haloperidol on procedural learning in man

The effects of an indirect dopamine-agonist, d-amphetamine, and a non-selective dopamine receptor antagonist, haloperidol, were investigated in normal male volunteers using a between-subjects double-blind design in a procedural learning task, thought mainly to involve unconscious/automatic learning. The results showed: (1) d-amphetamine facilitated response speed, whereas haloperidol inhibited it, in comparison to placebo; (2) the linear increase in procedural learning corresponded with pharmacological manipulation of degree of dopaminergic activity, i.e. subjects given haloperidol showed the least, and subjects given d-amphetamine the greatest, procedural learning. The implications of these findings are discussed in relation to investigation of abnormalities of procedural learning processes in schizophrenia. Received: 28 June 1996/Final version: 2 October 1996     

血细胞彩色图像的微机自动计数方法探讨

目的　探讨血细胞自动计数的方法。方法　采用微机彩色图像处理技术对血液细胞图像进行图像分割和细胞计数。结果　与人工计数相平行 ,相关系数r =0 .96 1～ 0 .997,P >0 .0 5。结论　该方法可行且计数相对精确、迅速、客观     

MR image segmentation using vector decomposition and probability techniques: A general model and its application to dual-echo images

A general model is developed for segmenting magnetic resonance images using vector decomposition and probabilfty techniques. Each voxel is assigned fractional volumes of q tissues from p differently weighted images (q ≤ p + 1) in the presence of partial-volume mixing, random noise, and other tissues. Compared wtth the eigenimage method, fewer differently weighted images are needed for segmenting the q tissues, and the contrast-to-noise ratio in the calculated fractional volumes is improved. The model can produce com-posrte tissue-type images similar to that of the probability methods, by comparing the fractional volumes assigned to different tissues on each voxel. A three-tissue (p = 2, q = 3) model is illustrated for segmenting three tissues from dual-echo images. M provides statistical analysis to the algebraic method. A three-compartment phantom is segmented for validation. Two clinical examples are presented.