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Long-patch Base Excision Repair

Long-patch Base Excision Repair

Since the discovery of the two BER pathways, there has been a debate about how a cell chooses one pathway over the other and the factors determining this selection. Numerous in vitro experiments have pointed out multiple determinants for the sub-pathway selection. These are:

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Updated: Jun 22, 2025

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

预测手动修复自动细分所需的努力.

Da He^1,2, Jayaram K Udupa¹, Yubing Tong¹

¹Medical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States.

medRxiv : the preprint server for health sciences

|July 1, 2024

概括

此摘要是机器生成的。

评估医疗图像自动细分需要超越重叠和距离的指标. 像Mendability Index (MI) 和深度学习模型这样的新指标可以更好地预测手动校正时间,改善放射学和瘤学工作流程.

关键词:

深度学习是一种深度学习.图像细分图像细分虚假性指数虚假性指数努力修复修复的努力.细分指标是细分的指标.

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Serial Block-Face Scanning Electron Microscopy SBF-SEM of Biological Tissue Samples

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相关实验视频

Last Updated: Jun 22, 2025

Quantifying Intermembrane Distances with Serial Image Dilations

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Published on: September 28, 2018

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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科学领域:

医学图像分析医学图像分析
放射学放射学是指放射学
辐射瘤学辐射瘤学

背景情况:

自动细分对于医学图像分析至关重要,影响放射学和瘤学效率.
目前的指标,如子系数 (DC) 和豪斯多夫距离 (HD),可能不反映临床手动校正努力.
需要准确的评估指标来指导卓越的自动细分技术的开发.

研究的目的:

调查与临床手动校正努力相关的细分指标.
将已建立的指标 (DC,HD,surDC,APL) 与新的混合指标 (可变性指数-MI) 进行比较.
探索深度学习来预测手动细分校正时间.

主要方法:

专家记录了修复时间,以量化手工校正力度.
在五个指标上进行了相关性和回归分析:DC,HD,表面DC (surDC),增加路径长度 (APL) 和MI.
训练有素的深度学习模型使用细分面具和原始图像来预测修复力度.

主要成果:

可修复性指数 (MI) 最好表示稀疏物体的修复力度.
对于非散射物体,豪斯多夫距离 (HD) 是最有效的.
深度学习模型准确地预测了修复努力,即使没有基础真相数据.

结论:

可变性指数 (MI) 和豪斯多夫距离 (HD) 显示出基于对象稀疏性的自动细分评估的前景.
深度学习提供了一种新的,高效的方法来评估和增强临床实践中的自细分技术.