An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN
نویسندگان
چکیده
منابع مشابه
Estimation Based on an Appropriate Choice of Loss Function
Some examples of absurd uniformly minimum variance unbiased estimators are discussed. Two reasons, argued in the literature, for having such estimators are lack of enough information in the available data and property of unbiasedness. In this paper, accepting both of these views, we show that an appropriate choice of loss function using a general concept of unbiasedness leads to risk unb...
متن کاملMRI estimation of the arterial input function in mice.
RATIONAL AND OBJECTIVES Dynamic contrast-enhanced (DCE) MRI offers the potential to provide quantitative maps of tumor perfusion parameters and is therefore expected to play an important role in the study of cancer in small animal models. Extraction of such information from DCE-MRI data requires a methodology for determination of the arterial input function (AIF) for the target tissues. An MRI ...
متن کاملAutomatic selection of arterial input function on dynamic contrast-enhanced MRI images: comparison of different methods
gadolinium images by deconvolution given the arterial input function, AIF(t), and tissue concentration, C(t),: C(t)=CBF٠[AIF(t)⊗R(t)], where R(t) is the tissue residue function. Often AIF is found by manually inspecting tracer concentration maps which is very time consuming and operator dependent. In our study we compare 5 methods of AIF automatic selection, including a novel one. The performan...
متن کاملH-CNN: Spatial Hashing Based CNN for 3D Shape Analysis
We present a novel spatial hashing based data structure to facilitate 3D shape analysis using convolutional neural networks (CNNs). Our method well utilizes the sparse occupancy of 3D shape boundary and builds hierarchical hash tables for an input model under different resolutions. Based on this data structure, we design two efficient GPU algorithms namely hash2col and col2hash so that the CNN ...
متن کاملMulti-region Two-Stream R-CNN for Action Detection
Motivation: I Previous work shows improvement with better proposal methods [1] I State-of-the-art CNN based action classi cation relies on multi-frame optical ow [2] I Object recognition is improved by multiple-region feature [3] Contribution: I We introduce a motion Region Proposal Network (RPN) I We show that multi-frame optical ow signi cantly improves action detection I We embed a multi-reg...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Neuroinformatics
سال: 2019
ISSN: 1662-5196
DOI: 10.3389/fninf.2019.00049