Tracking Metastatic Brain Tumors in Longitudinal Scans via Joint Image Registration and Labeling.

Nicha Chitphakdithai, Veronica L Chiang, James S Duncan
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引用次数: 7

Abstract

The treatment of metastatic brain tumors with stereotactic radiosurgery requires that the clinician first locate the tumors and measure their volumes. Thoroughly searching a patient scan for brain tumors and delineating the lesions can be a long and difficult task when done manually and is also prone to human error. In this paper, we present an automated method for detecting changes in brain tumor lesions over longitudinal scans to aide the clinician's task of determining tumor volumes. Our approach jointly registers the current image with a previous scan while estimating changes in intensity correspondences due to tumor growth or regression. We combine the label map with correspondence changes with tumor segmentations from a previous scan to estimate the metastases in the new image. Alignment and tumor tracking results show promise on 28 registrations using real patient data.

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通过联合图像配准和标记在纵向扫描中追踪脑转移瘤。
立体定向放射外科治疗脑转移瘤需要临床医生首先定位肿瘤并测量其体积。当手动完成时,彻底搜索患者的脑部肿瘤扫描并描绘病变可能是一项漫长而困难的任务,而且容易出现人为错误。在这篇论文中,我们提出了一种在纵向扫描中检测脑肿瘤病变变化的自动方法,以帮助临床医生确定肿瘤体积。我们的方法将当前图像与之前的扫描联合配准,同时估计由于肿瘤生长或消退引起的强度对应的变化。我们将标记图与先前扫描的肿瘤分割的对应变化相结合,以估计新图像中的转移。比对和肿瘤跟踪结果显示,使用真实患者数据进行的28次登记有望实现。
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Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy. Tracking Metastatic Brain Tumors in Longitudinal Scans via Joint Image Registration and Labeling.
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