Understanding and enhancing visual search performance in complex scenes

描述（由申请人提供）：了解和增强复杂场景中的视觉搜索性能癌症筛查可以挽救生命（例如，国家肺筛查试验研究，2011年）。每天，放射科医生都面临着困难，耗时的视觉搜索任务，如乳房X光检查和肺癌筛查。通常，癌症的迹象，例如肺结节或乳房中的微小异常，很难在异质背景中找到，并且被重叠的组织所掩盖。错过这些癌症的迹象可能会导致错误的诊断与生命或死亡的后果。因此，确定和解决这些关键搜索任务中存在的问题至关重要。拟议的研究旨在通过两种方式提高癌症筛查的搜索性能：首先，通过增强嵌入3D体积数据集中的肺结节的可见性。放射科医生通常通过滚动胸部CT来寻找肺结节。结节是大致球形的特征，在CT堆叠中跨越几个切片。有趣的是，专家报告说，肺结节在不断变化的视图中“弹出”的方式是它们存在的信号。我们提出了一种创新的方法，使用显着性算法，利用这种信号来增强胸部CT的方式，将注意力集中到场景的关键区域。第二，我们的目标是通过理解和利用非选择性的，“gist”般的处理乳房X线照片，以提高搜索性能。最近的研究表明，放射科专家可以在乳房X光片中检测到一个全局信号，在非常短的刺激暴露后，可以对正常和异常乳房进行随机分类。我们将首先训练新手成为更接近医疗任务的专家。随着专业知识的发展，我们将研究两种不同的神经相关性，它们可能在使用脑电图（EEG）的训练过程中演变，即P300和N2 pc。在其他设置中，这些措施可以信号注意力选择跨（P300）或内（N2 pc）简要介绍的图像。我们将采用一种新方法，利用机器学习对大脑信号进行实时解码。它允许神经签名，在响应一系列图像时被引出，用于对这些图像进行排序，以使其对观众产生隐含的“兴趣”。我们假设这些信息可以反馈给观察者/放射科医生作为信息来源，例如，可能表明图像或区域值得更多的审查。因此，本提案的主要目标是了解复杂视觉搜索任务中注意力的指导，并将这些知识应用于临床相关搜索任务（如癌症筛查）的改进。 公共卫生相关性：了解和增强复杂场景中的视觉搜索性能早期发现癌症可以挽救生命，但癌症筛查中的错误率（包括漏诊和误报）仍然太高。我们提出了两种方法来帮助放射科医生进行癌症筛查：1）我们将通过使用显着性算法来增强搜索显示，以将注意力引导到场景的关键区域。2)我们将利用神经信号引起的非选择性，“gist”样处理的医学图像作为一种新的支持乳房X光片的评价。

DESCRIPTION (provided by applicant): Understanding and enhancing visual search performance in complex scenes Cancer screening saves lives (e.g. National Lung Screening Trial Research, 2011). Every day, radiologists are faced with difficult, time-consuming visual search tasks like in mammography and lung cancer screening. Oftentimes signs of cancer, e.g. lung nodules or little abnormalities in a breast, are very hard to find against heterogeneous backgrounds and obscured by overlapping tissues. Missing these signs of cancer can result in wrong diagnoses with life or death consequences. It is therefore of key interest to identify and tackle the problems posed in these crucial search tasks. The proposed studies aim to improve search performance in cancer screening in two ways: First, by enhancing the visibility of lung nodules embedded in 3D volumetric datasets. Radiologists usually search for lung nodules by scrolling through stacks of chest CT. Nodules are roughly spherical features, spanning a few slices in a CT stack. Anecdotally, experts report that the way that lung nodules 'pop' in and out of the changing view is a signal to their presence. We propose an innovative approach using saliency algorithms that harness this signal to enhance chest CTs in a way that directs attention to crucial regions of a scene. Second, we aim to improve search performance by understanding and utilizing non-selective, 'gist'-like processing of mammograms. Recent work has shown that expert radiologists can detect a global signal in mammograms that allows for above-chance categorization of normal and abnormal breasts after very short exposures to the stimulus. We will first train novices to become experts in closer approximation to the medical tasks. As expertise develops, we will investigate two different neural correlates that might evolve in the course of training using electroencephalography (EEG), namely the P300 and the N2pc. In other settings, these measures can signal attentional selection either across (P300) or within (N2pc) briefly presented images. We will adapt a new method that exploits machine learning for real-time decoding of brain signals. It allows neural signatures, elicited in response to a sequence of images, to be used to rank those images in order of their implicit 'interest' to the viewer. We hypothesize that this information can be fed back to the observer/radiologist as a source of information that might, for example, suggest that an image or region deserves more scrutiny. The main goals of this proposal are therefore to understand the guidance of attention in complex visual search tasks and to apply this knowledge to improvements in clinically relevant search tasks like cancer screening. PUBLIC HEALTH RELEVANCE: Understanding and enhancing visual search performance in complex scenes Early detection of cancer can save lives, but error rates in cancer screening, both misses and false alarms, are still too high. We propose two ways of aiding cancer screening by radiologists: 1) We will enhance search displays by using saliency algorithms to direct attention to crucial regions of a scene. 2) We will utilize neural signals elicited by non-selective, 'gist'-like processing of medical images as a novel support for evaluation of mammograms.