• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on biomedical applications in molecular, structural, and functional imaging >Computerized segmentation algorithm with personalized atlases of murine MRIs in a SV40 Large T-antigen mouse mammary cancer model
【24h】

Computerized segmentation algorithm with personalized atlases of murine MRIs in a SV40 Large T-antigen mouse mammary cancer model

机译：SV40大型T抗原小鼠乳腺癌模型中带有鼠MRI个性化图谱的计算机分割算法。

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Quantities of MRI data, much larger than can be objectively and efficiently analyzed manually, are routinely generated in preclinical research. We aim to develop an automated image segmentation and registration pipeline to aid in analysis of image data from our high-throughput 9.4 Tesla small animal MRI imaging center. T2-weighted, fat-suppressed MRIs were acquired over 4 life-cycle time-points [up to 12 to 18 weeks] of twelve C3(1) SV40 Large T-antigen mice for a total of 46 T2-weighted MRI volumes; each with a matrix size of 192 × 256, 62 slices, in plane resolution 0.1 mm, and slice thickness 0.5 mm. These image sets were acquired with the goal of tracking and quantifying progression of mammary intraepithelial neoplasia (MIN) to invasive cancer in mice, believed to be similar to ductal carcinoma in situ (DCIS) in humans. Our segmentation algorithm takes 2D seed-points drawn by the user at the center of the 4 co-registered volumes associated with each mouse. The level set then evolves i2n 3D from these 2D seeds. The contour evolution incorporates texture information, edge information, and a statistical shape model in a two-step process. Volumetric DICE coefficients comparing the automatic with manual segmentations were computed and ranged between 0.75 and 0.58 for averages over the 4 life-cycle time points of the mice. Incorporation of these personalized atlases with intra and inter mouse registration is expected to enable locally and globally tracking of the morphological and textural changes in the mammary tissue and associated lesions of these mice.
机译：临床前研究通常会生成MRI数据，其数量要比客观和有效地手动分析要大得多。我们旨在开发自动图像分割和配准管道，以帮助分析来自我们的高通量9.4特斯拉小动物MRI成像中心的图像数据。在12个C3（1）SV40大型T抗原小鼠的4个生命周期时间点（长达12至18周）内，获得了T2加权，脂肪抑制的MRI，共计46个T2加权MRI体积;每个矩阵尺寸为192×256，有62个切片，平面分辨率为0.1 mm，切片厚度为0.5 mm。获取这些图像集的目的是跟踪和量化小鼠乳腺上皮内瘤变（MIN）到浸润性癌症的进展，据信与人类原位导管癌（DCIS）相似。我们的分割算法采用用户在与每只鼠标关联的4个共同注册体积的中心绘制的2D种子点。然后，级别集从这些2D种子演变为2D 3D。轮廓演变在两步过程中结合了纹理信息，边缘信息和统计形状模型。计算了自动和手动分割的体积DICE系数，其在小鼠4个生命周期时间点的平均值介于0.75和0.58之间。这些个性化地图集与小鼠内和小鼠间配准的结合有望实现局部和全局追踪这些小鼠的乳腺组织和相关病变的形态和质地变化。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号