基于图谱的全身磁共振图像可解释年龄预测
Atlas-Based Interpretable Age Prediction In Whole-Body MR Images
AI总结：
本研究针对全身磁共振图像开展基于图谱的可解释年龄预测，通过Grad-CAM确定个体年龄预测关键区域，结合配准技术生成群体重要性图谱，揭示脊柱、背部固有肌肉及心脏区域的预测价值，并分析衰老速度差异。
摘要：
年龄预测是医学评估和研究的重要组成部分，通过突出实际年龄与生物年龄之间的潜在差异，有助于检测疾病和异常衰老。为了更好地理解不同身体部位的年龄相关变化，我们利用全身3D图像大规模研究人体衰老过程。我们采用Grad-CAM方法确定对年龄预测最具影响力的身体区域；为了将分析扩展到个体之外，我们使用配准技术生成群体层面的重要性图谱，展示整个队列中身体最具预测性的区域。研究表明，对全身完整3D体积的分析和群体层面的研究能为哪些身体部位在年龄预测中起关键作用提供重要见解。结果揭示了三个主要关注区域：脊柱、背部固有肌肉和预测重要性最高的心脏区域。最后，我们研究了表现出加速衰老和减速衰老的受试者之间的差异。
英文摘要：
Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting potential discrepancies between chronological and biological age. To improve understanding of age-related changes in various body parts, we investigate the ageing of the human body on a large scale by using whole-body 3D images. We utilise the Grad-CAM method to determine the body areas most predictive of a person's age. In order to expand our analysis beyond individual subjects, we employ registration techniques to generate population-wide importance maps that show the most predictive areas in the body for a whole cohort of subjects. We show that the investigation of the full 3D volume of the whole body and the population-wide analysis can give important insights into which body parts play the most important roles in predicting a person's age. Our findings reveal three primary areas of interest: the spine, the autochthonous back muscles, and the cardiac region, which exhibits the highest importance. Finally, we investigate differences between subjects that show accelerated and decelerated ageing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
