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Dimensional Analysis

Dimensional Analysis

Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...

Uniform Depth Channel Flow

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...

Uniform Depth Channel Flow: Problem Solving

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...

Linearization and Approximation

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...

Application of Linearization and Approximation

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

Indefinite Integrals

Indefinite Integrals

The water inflow rate into a storage tank is not constant but increases over time. Initially, the pump delivers water at a rate of 5 L/min. However, the inflow rate increases by 2 L/min for each additional minute due to rising pressure or system adjustments. This scenario can be described mathematically by a linear function:It is necessary to integrate the inflow rate function to measure the total volume of water added to the tank over time. The total water volume V(t) is obtained by performing...

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Updated: Jun 14, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

カロサール角度定量化のための自動化されたディープラーニングパイプライン

Siavash Shirzadeh Barough¹, Murat Bilgel², Catalina Ventura¹

¹Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

medRxiv : the preprint server for health sciences

|September 2, 2025

まとめ

この要約は機械生成です。

新しいディープラーニング・フレームワークは,MRIスキャンからカロソール角度 (CA) の測定を自動化し,正常圧力水頭症 (NPH) の診断を改善します. この信頼性の高い方法は,NPHの早期発見と臨床評価を促進します.

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A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

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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

関連する実験動画

Last Updated: Jun 14, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

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

科学分野:

神経イメージング
医療における人工知能
神経変性疾患

背景:

正常圧水頭症 (NPH) は,症状が重複し,イメージングバイオマーカーの手作業分析が時間がかかるため,診断が不十分である.
カルロスアングル (CA) のような重要な診断マーカーは,しばしば解釈の変動性がある.

研究の目的:

完全自動化された深層学習フレームワークを開発し,MRIスキャンからカロソール角度 (CA) の正確な測定を行う.
NPHの診断のための手動CA測定に強固で再現可能な代替手段を提供すること.

主な方法:

このフレームワークは,ランドマーク検出 (AC,PC) のためのBrainSignsNETと,横断心室セグメンテーションのためのUNetベースのネットワークを統合しています.
MRIスキャンは,CA計算のためにAC-PC線に垂直に冠状のスライスを使用して事前処理および分析されます.

主要な成果:

自動フレームワークは手動測定と高い一致性 (r = 0. 98, p < 0. 001) と2. 95度の低い平均絶対誤差 (MAE) を示した.
エヴァンズ指数 (EI) に依存せず,患者様ごとに一致した結果でした.

結論:

自動化されたCA測定フレームワークは,手作業の代替手段として信頼性と再現性を提供します.
このツールは研究や臨床での NPH の早期発見と診断を改善する大きな可能性を秘めています