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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

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相关实验视频

Updated: Jan 18, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

一种基于物理的深度学习可变形医疗图像注册方法,基于神经ODEs.

Amirhossein Amiri-Hezaveh¹, Shelly Tan¹, Qing Deng¹

¹School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA.

International journal of computer vision

|September 12, 2025

概括

一种新的无监督机器学习方法使用固体力学原理对准医疗图像. 这种方法准确地模拟了大变形和生物生长,在脑成像和组织再生研究中显示出前景.

关键词:

大脑缩 - 大脑缩大脑发育大脑发育大脑的注册脑的注册可变形图像的注册方式增长和重建的过程.医疗图像分析医学图像分析神经常规微分方程神经常规微分方程基于物理学的神经网络.斑马鱼的生物物理学

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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相关实验视频

Last Updated: Jan 18, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

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Published on: April 12, 2024

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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Published on: November 23, 2019

科学领域:

生物物理学的生物物理.
固体力学固体力学是什么
机器学习机器学习

背景情况:

准确的医学图像注册对于理解生物过程和疾病进展至关重要.
现有的方法与大变形和复杂的生物生长动态作斗争.

研究的目的:

引入无监督机器学习方法用于医疗图像注册.
纳入大变形弹性,生长和重塑的原则.
在各种生物和医学成像数据集上验证该方法.

主要方法:

基于最小潜在能量的无监督机器学习方法.
两个步骤的过程:几何记录的预测步骤和物理执行的校正步骤.
使用不相似度措施,规范化术语和潜在能量最小化.

主要成果:

成功地注册了具有大,不均变形的医疗图像.
对大脑数据的现有方法进行了竞争性表现.
应用于斑马鱼翅膀再生,大脑缩评估和胎儿大脑发育.

结论:

拟议的框架实现了高质量的医疗图像注册.
有效地解决大变形弹性平衡方程和生长/重塑动力学.
从成像数据中分析复杂的生物变化提供了一种多功能工具.