scholarly journals Sequential U-Net Architecture for Automatic Femoral Artery Segmentation in Ultrasound Images

2021 ◽  
Vol 7 (1) ◽  
pp. 158-161
Author(s):  
Ana Estrada Lugo ◽  
Niclas Bockelmann ◽  
Felix von Haxthausen
Keyword(s):  
Femoral Artery ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Segmentation Result ◽  
Ultrasound Probe ◽  
Sequential Structure ◽  
Tracked Ultrasound ◽  
Vessel Volume ◽  
2D Ultrasound

Abstract This work compares three different approaches to automatically segment the femoral artery from 2D ultrasound images. Two of the architectures follow a sequential structure, where each ultrasound image is considered a slice of the whole vessel volume, and its previous segmentation result will be part of the input, thus leading to a spatial prior. The Dice score on test data show a better performance on the baseline U-Net (0.819) compared to the sequential U-Net approaches (0.633, 0.725) for the femoral artery segmentation. This could be attributed to the misalignment of the slices being used in those networks. A possible improvement could be assumed in the implementation of a spatially calibrated and tracked ultrasound probe. Overall, these results indicate promising approaches for an automatic segmentation of the femoral artery using 2D ultrasound data.

scholarly journals Versatile Low-Cost Volumetric 3D Ultrasound Imaging Using Gimbal-Assisted Distance Sensors and an Inertial Measurement Unit

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6613
Author(s):  
Taehyung Kim ◽  
Dong-Hyun Kang ◽  
Shinyong Shim ◽  
Maesoon Im ◽  
Bo Kyoung Seo ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Low Cost ◽  
Ultrasound Image ◽  
3D Ultrasound ◽  
Measurement Unit ◽  
Ultrasound Images ◽  
Ultrasound Probe ◽  
Inertial Measurement ◽  
Position Data ◽  
2D Ultrasound

This study aims at creating low-cost, three-dimensional (3D), freehand ultrasound image reconstructions from commercial two-dimensional (2D) probes. The low-cost system that can be attached to a commercial 2D ultrasound probe consists of commercial ultrasonic distance sensors, a gimbal, and an inertial measurement unit (IMU). To calibrate irregular movements of the probe during scanning, relative position data were collected from the ultrasonic sensors that were attached to a gimbal. The directional information was provided from the IMU. All the data and 2D ultrasound images were combined using a personal computer to reconstruct 3D ultrasound image. The relative position error of the proposed system was less than 0.5%. The overall shape of the cystic mass in the breast phantom was similar to those from 2D and sections of 3D ultrasound images. Additionally, the pressure and deformations of lesions could be obtained and compensated by contacting the probe to the surface of the soft tissue using the acquired position data. The proposed method did not require any initial marks or receivers for the reconstruction of a 3D ultrasound image using a 2D ultrasound probe. Even though our system is less than $500, a valuable volumetric ultrasound image could be provided to the users.

Automatic Segmentation of the Cerebral Falx and Adjacent Gyri in 2D Ultrasound Images

2015 ◽  
pp. 287-292 ◽  
Author(s):  
Jennifer Nitsch ◽  
Jan Klein ◽  
Dorothea Miller ◽  
Ulrich Sure ◽  
Horst K. Hahn
Keyword(s):  
Automatic Segmentation ◽  
Ultrasound Images ◽  
2D Ultrasound

Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network

2020 ◽  
pp. 1-16
Author(s):  
Ling Zhang ◽  
Yan Zhuang ◽  
Zhan Hua ◽  
Lin Han ◽  
Cheng Li ◽  
...  
Keyword(s):  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ultrasound Images ◽  
Semantic Features ◽  
Low Contrast ◽  
Automated Method

BACKGROUND: Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound images is still a challenging task. OBJECTIVE: This study proposes an automatic detection algorithm to locate nodules in B ultrasound images and Doppler ultrasound images. This method can be used to screen thyroid nodules and provide a basis for subsequent automatic segmentation and intelligent diagnosis. METHODS: We develop and optimize an improved YOLOV3 model for detecting thyroid nodules in ultrasound images with B-mode and Doppler mode. Improvements include (1) using the high-resolution network (HRNet) as the basic network for gradually extracting high-level semantic features to reduce the missed detection and misdetection, (2) optimizing the loss function for single target detection like nodules, and (3) obtaining the anchor boxes by clustering the candidate frames of real nodules in the dataset. RESULTS: The experimental results of applying to 8000 clinical ultrasound images show that the new method developed and tested in this study can effectively detect thyroid nodules. The method achieves 94.53% mean precision and 95.00% mean recall. CONCLUTIONS: The study demonstrates a new automated method that enables to achieve high detection accuracy and effectively locate thyroid nodules in various ultrasound images without any user interaction, which indicates its potential clinical application value for the thyroid nodule screening.

Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN

2021 ◽  
pp. 96-105
Author(s):  
Kanta Miura ◽  
Koichi Ito ◽  
Takafumi Aoki ◽  
Jun Ohmiya ◽  
Satoshi Kondo
Keyword(s):  
Pose Estimation ◽  
Ultrasound Image ◽  
Image Sequences ◽  
Ultrasound Probe ◽  
2D Ultrasound

Segmentation Methods in Ultrasound Images

2011 ◽  
pp. 377-390
Author(s):  
Farhang Sahba
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
The Body ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Medical Test ◽  
Segmentation Methods ◽  
Segmentation Task

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

Segmentation Methods in Ultrasound Images

2009 ◽  
pp. 473-487
Author(s):  
Farhang Sahba
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
The Body ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Medical Test ◽  
Segmentation Methods ◽  
Segmentation Task

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

Feature Based Active Contour Method for Automatic Detection of Breast Lesions Using Ultrasound Images

2014 ◽  
Vol 573 ◽  
pp. 471-476 ◽  
Author(s):  
Telagarapu Prabhakar ◽  
S. Poonguzhali
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Contour Method ◽  
Speckle Reduction ◽  
Initial Contour ◽  
Ultrasound Images ◽  
Active Contour Method

Breast cancer has been increasing over the past three decades. Early detection of breast cancer is crucial for an effective treatment. Mammography is used for early detection and screening. Especially for young women, mammography procedures may not be very comfortable. Ultrasound has been one of the most powerful techniques for imaging organs and soft tissue structure in the human body. It has been used for breast cancer detection because of its non-invasive, sensitive to dense breast, low positive rate and cheap cost. But due to the nature of ultrasound image, the image suffers from poor quality caused by speckle noise. These make the automatic segmentation and classification of interested lesion difficult. One of the frequently used segmentation method is active contour. If this initial contour of active contour method is selected outside the region of interest, final segmentation and classification would be definitely incorrect. So, mostly the initial contour is manually selected in order to avoid incorrect segmentation and classification. Here implementing a method which was able to locate the initial contour automatically within the multiple lesion regions by using the wavelet soft threshold speckle reduction method, statistical features of the lesion regions and neural network and also we are able to automatically segment the lesion regions properly. This will help the radiologist to identify the lesion boundary automatically.

scholarly journals A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2D ultrasound images

2016 ◽  
Vol 61 (3) ◽  
pp. 1095-1115 ◽  
Author(s):  
Lei Zhang ◽  
Xujiong Ye ◽  
Tryphon Lambrou ◽  
Wenting Duan ◽  
Nigel Allinson ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ultrasound Images ◽  
Fetal Head ◽  
2D Ultrasound

scholarly journals Interpreting Ultrasound Images for Accurate Epidural Needle Insertion

2017 ◽  
Author(s):  
Neil Vaughan ◽  
Venketesh N. Dubey
Keyword(s):  
Image Processing ◽  
Epidural Space ◽  
Human Error ◽  
Ultrasound Image ◽  
Low Frequency ◽  
Needle Insertion ◽  
Epidural Needle ◽  
Ultrasound Images ◽  
Gradient Based ◽  
2D Ultrasound

This work presents development and testing of image processing algorithms for the automatic detection of landmarks within ultrasound images. The aim was to automate ultrasound analysis, for use during the process of epidural needle insertion. For epidural insertion, ultrasound is increasingly used to guide the needle into the epidural space. Ultrasound can improve the safety of epidural and was recommended by the 2008 NICE guidelines (National Institute for Health and Care Excellence). Without using ultrasound, there is no way for the anaesthetist to observe the location of the needle within the ligaments requiring the use of their personal judgment which may lead to injury. If the needle stops short of the epidural space, the anaesthetic is ineffective. If the needle proceeds too deep, it can cause injuries ranging from headache, to permanent nerve damage or death. Ultrasound of the spine is particularly difficult, because the complex bony structures surrounding the spine limit the ultrasound beam acoustic windows [1]. Additionally, the important structures for epidural that need to be observed are located deeper than other conventional procedures such as peripheral nerve block. This is why a low frequency, curved probe (2–5 MHz) is used, which penetrates deeper but decreases in resolution. The benefits of automating ultrasound are to enable real-time ultrasound analysis on the live video, mitigate human error, and ensure repeatability by avoiding variation in perception by different users. Previous ultrasound image processing for epidural research used speckle image enhancement with canny and gradient based methods for bone detection [2]. A clinical trial with 39 patients had success detecting the ligamentum flavum (LF) from ultrasound by algorithms in 87% of patients. Echogenic needles and catheters are now becoming available which are enhanced for extra ultrasound visibility. The Epimed UltraKath ULTRA-KATH™ [3] has a patented design to maximize visibility under ultrasound [4]. The Echogenic Tuohy Needle also includes imprints on the needle tip that reflects ultrasound, allowing for better visualization. Curved needles can also be detected in 2D ultrasound images [5].

scholarly journals Needle Placement for Piriformis Injection Using 3-D Imaging

2013 ◽  
Vol 3;16 (3;5) ◽  
pp. E301-E310 ◽  
Author(s):  
Steven R. Clendenen
Keyword(s):  
Ultrasound Image ◽  
Pain Syndrome ◽  
Leg Pain ◽  
Computer Assisted ◽  
Piriformis Syndrome ◽  
Ultrasound Probe ◽  
Tracked Ultrasound ◽  
The Mean ◽  
The Magnetic Field

Piriformis syndrome is a pain syndrome originating in the buttock and is attributed to 6% – 8% of patients referred for the treatment of back and leg pain. The treatment for piriformis syndrome using fluoroscopy, computed tomography (CT), electromyography (EMG), and ultrasound (US) has become standard practice. The treatment of Piriformis Syndrome has evolved to include fluoroscopy and EMG with CT guidance. We present a case study of 5 successful piriformis injections using 3-D computer-assisted electromagnet needle tracking coupled with ultrasound. A 6-degree of freedom electromagnetic position tracker was attached to the ultrasound probe that allowed the system to detect the position and orientation of the probe in the magnetic field. The tracked ultrasound probe was used to find the posterior superior iliac spine. Subsequently, 3 points were captured to register the ultrasound image with the CT or magnetic resonance image scan. Moreover, after the registration was obtained, the navigation system visualized the tracked needle relative to the CT scan in real-time using 2 orthogonal multi-planar reconstructions centered at the tracked needle tip. Conversely, a recent study revealed that fluoroscopically guided injections had 30% accuracy compared to ultrasound guided injections, which tripled the accuracy percentage. This novel technique exhibited an accurate needle guidance injection precision of 98% while advancing to the piriformis muscle and avoiding the sciatic nerve. The mean (± SD) procedure time was 19.08 (± 4.9) minutes. This technique allows for electromagnetic instrument tip tracking with realtime 3-D guidance to the selected target. As with any new technique, a learning curve is expected; however, this technique could offer an alternative, minimizing radiation exposure. Key words: Piriformis, electromagnetic, ultrasound, fluoroscopy, injection, 3-D imaging.

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