Effect of 3D-Printed Hearts Used in Left Ventricular Outflow Tract Obstruction: A Multicenter Study

Wang, Xianzhi; Liang, Jixiang; Zhang, Wenlin; Xue, Chunzhu; He, Yang; Zhang, Gen; Li, Dianyuan

doi:10.21203/rs.3.rs-1206928/v1

Cited by 3 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the hemodynamic obstruction of the left ventricular outflow tract (LVOT), as well as the difficulty of the operation due to complex anatomical relationships of hypertrophic cardiomyopathy (HCM) and poor visualization of the left ventricular cavity, 3D printing can be used primarily to treat hypertrophic cardiomyopathy. (18). 3d-printed models of predominant cardiac neoplasms have been utilized in identifying the cardiac tumor growth and structures which are at risk to determine appropriate surgical methods and, additionally, in valve-in-valve procedures (3).…”

Section: Role Of 3d Printing In Cardiologymentioning

confidence: 99%

A Critical Review on Role of 3D-Printer in Cardiology

Kalal

2023

IJRASET

View full text Add to dashboard Cite

The focus of recent studies has been on 3D printing technologies. Models are manufactured using magnetic resonance imaging, computed tomography, or echocardiography in three dimensions (3D printing) , and the process is also known as additive manufacturing. The debate about 3D printing's function in cardiology is the main focus of current studies . To decrease the mortality rate due to heart related diseases like cardiac arrest , which has been tremendous in recent times, 3D printed models serve as a savior of life. To illustrate, during COVID-19, the coronavirus created a global pandemic and a huge global demand for medical equipment. The shortage of time and the high level of social distancing make it difficult for the government and medical professionals to face the pandemic. In those critical times, 3D printing models were used to develop medical equipment and explore its potential by addressing the shortage of equipment. The physical models are printed using various methods like Fused Deposition Modeling, Polyjet, Stereolithography, Selective Laser Melting, and Sintering. This also enhances the practical knowledge of students and surgeons and revives confidence in the patients and their families.

show abstract

Section: Role Of 3d Printing In Cardiologymentioning

confidence: 99%

A Critical Review on Role of 3D-Printer in Cardiology

Kalal

2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…In addition to directly taking the monocular image as input, pseudo-lidar based approaches [35,[41][42][43]46] adopt a depth estimation network [14] to convert the 2D images into 3D point cloud and then apply a point cloud detector on them. Although they achieve superior performance, the input transformation requires an extra depth estimation module during inference, leading to high latency.…”

Section: Monocular 3d Detectionmentioning

confidence: 99%

Exploring Geometric Consistency for Monocular 3D Object Detection

Lian

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the illposed depth estimation. We first conduct a thorough analysis to reveal how existing methods fail to consistently localize objects when different geometric shifts occur. In particular, we design a series of geometric manipulations to diagnose existing detectors and then illustrate their vulnerability to consistently associate the depth with object apparent sizes and positions. To alleviate this issue, we propose four geometry-aware data augmentation approaches to enhance the geometric consistency of the detectors. We first modify some commonly used data augmentation methods for 2D images so that they can maintain geometric consistency in 3D spaces. We demonstrate such modifications are important. In addition, we propose a 3D-specific image perturbation method that employs the camera movement. During the augmentation process, the camera system with the corresponding image is manipulated, while the geometric visual cues for depth recovery are preserved. We show that by using the geometric consistency constraints, the proposed augmentation techniques lead to improvements on the KITTI and nuScenes monocular 3D detection benchmarks with state-of-the-art results. In addition, we demonstrate that the augmentation methods are well suited for semisupervised training and cross-dataset generalization.

show abstract

“…monocular 3D detection models [28,34,48,49,65] alleviate the need of LiDAR sensors, making the self-driving system easier to be applied. However, there is still a challenging problem that limits the application of 3D object detection with pure camera vision data.…”

Section: Introductionmentioning

confidence: 99%

Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency

Tao¹,

Han²,

Qiu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Monocular 3D object detection has become a mainstream approach in automatic driving for its easy application. A prominent advantage is that it does not need Li-DAR point clouds during the inference. However, most current methods still rely on 3D point cloud data for labeling the ground truths used in the training phase. This inconsistency between the training and inference makes it hard to utilize the large-scale feedback data and increases the data collection expenses. To bridge this gap, we propose a new weakly supervised monocular 3D objection detection method, which can train the model with only 2D labels marked on images. To be specific, we explore three types of consistency in this task, i.e. the projection, multi-view and direction consistency, and design a weakly-supervised architecture based on these consistencies. Moreover, we propose a new 2D direction labeling method in this task to guide the model for accurate rotation direction prediction. Experiments show that our weakly-supervised method achieves comparable performance with some fully supervised methods. When used as a pre-training method, our model can significantly outperform the corresponding fullysupervised baseline with only 1/3 3D labels.

show abstract

Effect of 3D-Printed Hearts Used in Left Ventricular Outflow Tract Obstruction: A Multicenter Study

Cited by 3 publications

References 8 publications

A Critical Review on Role of 3D-Printer in Cardiology

A Critical Review on Role of 3D-Printer in Cardiology

Exploring Geometric Consistency for Monocular 3D Object Detection

Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency

Contact Info

Product

Resources

About