Automated Vein Segmentation from NIR Images Using a Mixer-UNet Model

Ji, Jiarui; Zhao, Yibo; Xie, Tenghui; Du, Fuxin; Qi, Peng

doi:10.1007/978-3-031-13841-6_6

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…There are two approaches which are present in U-Net architecture for segmenting biomedical pictures. The initial approach which is also known as encoder shortens.Context is captured by the encoder using a small feature map [35][36][37]. Max-pooling and convolution layers like Vgg-16 make up the encoder.…”

Section: Segmentationmentioning

confidence: 99%

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Yadlapalli¹,

Bhavana

2022

SCPE

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Medical image processing involves using and examining 3D human body images, which are most frequently acquired through a computed tomography scanner, to diagnose disorders. Medical image process- ing helps radiologists, engineers, and clinicians better comprehend the anatomy of specific patients or groups of patients. Due to recent advancements in deep learn ing techniques, the study of medical image analysis is now a quickly expanding area of research. Interstitial Lung Disease is a chronic lung disease that worsens with time. This condition cannot be completely treated when the lungs have been damaged. Early detection, on the other hand, aids in the control of the disease. It causes lung scarring as a result. The first methodology characterizes lung tissue utilizing first order statistics, grey live occurrence, run length matrices, and fractal analysis. It was suggested by Uppaluri et al in one instance. In the pre-processing step, patients' CT scans are presented using various color map models for better understanding of data-set. and also for determining the patients final Force Vital Capacity and Confidence values using a Pytorch model with leaky relu activation function. These variables can be used to determine whether a person has a disease. Segmentation is a crucial stage in employing a computer assisted diagnosis system to estimate interstitial lung disease. Accurate segmentation of aberrant lung is essential for a trustworthy computer-aided illness diagnosis. Using separate training, validation, and test sets, we proposed an efficient deep learning model using Unet architecture and Densenet121 to segment lungs with Interstitial Lung Disease. The proposed segmentation model distinguishes the exact lung region from the ct slice background. To train and evaluate the algo rithm, 176 sparsely annotated Computed Tomography scans were utilized. The training was completed in a supervised and end to end manner. Contrary to current approaches, the suggested method yields accurate segmentation results without the requirement for re-initialization. We were able to achieve an accuracy of 92.59 percent after training the proposed model with Nvidia's CUDA GPU.

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Section: Segmentationmentioning

confidence: 99%

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Yadlapalli¹,

Bhavana

2022

SCPE

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“…The prototype was designed based on binocular near-infrared imaging and pressure sensing technologies. In 2021, Qi et al from Tongji University developed a compact venipuncture robot VeniBot [16, 17], adopting binocular near-infrared and ultrasound for vascular imaging. They also proposed a novel deep-learning algorithm for automatic navigation of puncture devices.…”

Section: Introductionmentioning

confidence: 99%

A trocar puncture robot for assisting venipuncture blood collection

Yang,

Wen,

et al. 2024

Robotica

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The venous blood test is a prevalent auxiliary medical diagnostic method. Venous blood collection equipment can improve blood collection’s success rate and stability, reduce the workload of medical staff, and improve the efficiency of diagnosis and treatment. This study proposed a rigid-flexible composite puncture (RFCP) strategy, based on which a small 7-degree-of-freedom (DOF) auxiliary venipuncture blood collection (VPBC) robot using a trocar needle was designed. The robot consists of a position and orientation adjustment mechanism and a RFCP end-effector, which can perform RFCP to avoid piercing the blood vessel’s lower wall during puncture. The inverse kinematics solution and validation of the robot were analyzed based on the differential evolution algorithm, after which the quintic polynomial interpolation algorithm was applied to achieve the robot trajectory planning control. Finally, the VPBC robot prototype was developed for experiments. The trajectory planning experiment verified the correctness of the inverse kinematics solution and trajectory planning, and the composite puncture blood collection experiment verified the feasibility of the RFCP strategy.

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“…Current research in the field of intelligent injection robotics focuses on vein imaging [ 10 , 11 ], detection and segmentation [ 12 , 13 ], decision on needle insertion point location [ 14 , 15 ], and robot system design [ 16 , 17 ]. At the same time, fewer studies are related to the needle insertion angle for dorsal hand intravenous injection robots.…”

Section: Introductionmentioning

confidence: 99%

Decision Method of Optimal Needle Insertion Angle for Dorsal Hand Intravenous Robot

Zhu

Zhang

et al. 2023

Sensors

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In the context of COVID-19, the research on various aspects of the venipuncture robot field has become increasingly hot, but there has been little research on robotic needle insertion angles, primarily performed at a rough angle. This will increase the rate of puncture failure. Furthermore, there is sometimes significant pain due to the patients’ differences. This paper investigates the optimal needle entry angle decision for a dorsal hand intravenous injection robot. The dorsal plane of the hand was obtained by a linear structured light scan, which was used as a basis for calculating the needle entry angle. Simulation experiments were also designed to determine the optimal needle entry angle. Firstly, the linear structured optical system was calibrated and optimized, and the error function was constructed and solved iteratively by the optimization method to eliminate measurement error. Besides, the dorsal hand was scanned to obtain the spatial point clouds of the needle entry area, and the least squares method was used to fit it to obtain the dorsal hand plane. Then, the needle entry angle was calculated based on the needle entry area plane. Finally, the changes in the penetration force under different needle entry angles were analyzed to determine the optimal needle insertion angle. According to the experimental results, the average error of the optimized structured light plane position was about 0.1 mm, which meets the needs of the project, and a large angle should be properly selected for needle insertion during the intravenous injection.

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Automated Vein Segmentation from NIR Images Using a Mixer-UNet Model

Cited by 6 publications

References 10 publications

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

A trocar puncture robot for assisting venipuncture blood collection

Decision Method of Optimal Needle Insertion Angle for Dorsal Hand Intravenous Robot

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