scholarly journals Temporal compressive imaging reconstruction based on a 3D-CNN network

2022 ◽  
Author(s):  
Linxia Zhang ◽  
Edmund Lam ◽  
Jun Ke
Keyword(s):  
Compressive Imaging ◽  
Imaging Reconstruction ◽  
3D Cnn

scholarly journals Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

2021 ◽  
Author(s):  
Alexander Bigalke ◽  
Lasse Hansen ◽  
Jasper Diesel ◽  
Mattias P. Heinrich
Keyword(s):  
Point Cloud ◽  
Drug Dosage ◽  
Sensor Data ◽  
Patient Specific ◽  
Point Cloud Data ◽  
Weight Estimation ◽  
Visual Sensor ◽  
Cloud Data ◽  
Emergency Situations ◽  
3D Cnn

Abstract Purpose Body weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in emergency situations. Machine learning-based methods have recently been shown to perform accurate weight estimation from point cloud data. The proposed methods, however, are designed for controlled conditions in terms of visibility and position of the patient, which limits their practical applicability. In this work, we aim to decouple accurate weight estimation from such specific conditions by predicting the weight of covered patients from voxelized point cloud data. Methods We propose a novel deep learning framework, which comprises two 3D CNN modules solving the given task in two separate steps. First, we train a 3D U-Net to virtually uncover the patient, i.e. to predict the patient’s volumetric surface without a cover. Second, the patient’s weight is predicted from this 3D volume by means of a 3D CNN architecture, which we optimized for weight regression. Results We evaluate our approach on a lying pose dataset (SLP) under two different cover conditions. The proposed framework considerably improves on the baseline model by up to $${16}{\%}$$ 16 % and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to $${52}{\%}$$ 52 % . Conclusion We present a novel pipeline to estimate the weight of patients, which are covered by a blanket. Our approach relaxes the specific conditions that were required for accurate weight estimates by previous contactless methods and thus constitutes an important step towards fully automatic weight estimation in clinical practice.

Classification of Lung Nodules Based on GAN and 3D CNN

2020 ◽  
Author(s):  
Bin Sun ◽  
Fengyin Liu ◽  
Yusun Zhou ◽  
Shaolei Jin ◽  
Qiang Li ◽  
...  
Keyword(s):  
Lung Nodules ◽  
3D Cnn

scholarly journals Modeling and image quality enhancement for dynamic compressive imaging system

2021 ◽  
Vol 15 ◽  
pp. 174830262110080
Author(s):  
Changjun Zha* ◽  
Qian Zhang* ◽  
Huimin Duan
Keyword(s):  
Image Quality ◽  
Imaging System ◽  
Quality Enhancement ◽  
Target Image ◽  
Compressive Imaging ◽  
Image Quality Enhancement ◽  
System A ◽  
Single Column ◽  
The Impact

Traditional single-pixel imaging systems are aimed mainly at relatively static or slowly changing targets. When there is relative motion between the imaging system and the target, sizable deviations between the measurement values and the real values can occur and result in poor image quality of the reconstructed target. To solve this problem, a novel dynamic compressive imaging system is proposed. In this system, a single-column digital micro-mirror device is used to modulate the target image, and the compressive measurement values are obtained for each column of the image. Based on analysis of the measurement values, a new recovery model of dynamic compressive imaging is given. Differing from traditional reconstruction results, the measurement values of any column of vectors in the target image can be used to reconstruct the vectors of two adjacent columns at the same time. Contingent upon characteristics of the results, a method of image quality enhancement based on an overlapping average algorithm is proposed. Simulation experiments and analysis show that the proposed dynamic compressive imaging can effectively reconstruct the target image; and that when the moving speed of the system changes within a certain range, the system reconstructs a better original image. The system overcomes the impact of dynamically changing speeds, and affords significantly better performance than traditional compressive imaging.

scholarly journals A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thao Thi Ho ◽  
Taewoo Kim ◽  
Woo Jin Kim ◽  
Chang Hyun Lee ◽  
Kum Ju Chae ◽  
...  
Keyword(s):  
Neural Network ◽  
Pulmonary Function Tests ◽  
Airway Disease ◽  
Chronic Obstructive ◽  
Respiratory Disorder ◽  
Obstructive Pulmonary Disease ◽  
Response Mapping ◽  
Small Airway Disease ◽  
3D Cnn ◽  
Parametric Response

AbstractChronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.

scholarly journals Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ossama Mahmoud ◽  
Mahmoud El-Sakka ◽  
Barry G. H. Janssen
Keyword(s):  
Machine Learning ◽  
Blood Flow ◽  
Human Error ◽  
Image Data ◽  
Video Microscopy ◽  
Microvascular Blood Flow ◽  
Image Processing Algorithm ◽  
Step Algorithm ◽  
3D Cnn

AbstractMicrovascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.

Colorful holographic imaging reconstruction based on one thin phase plate

2014 ◽  
Author(s):  
Jing Zhu ◽  
Qiang Song ◽  
Jian Wang ◽  
Weirui Yue ◽  
Fang Zhang ◽  
...  
Keyword(s):  
Phase Plate ◽  
Holographic Imaging ◽  
Imaging Reconstruction
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