From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Deep Learning in Medical Ultrasound Analysis: A Review

A variety of advanced image analysis methods have been under the development for ultrasound-guided interventions. Unfortunately, the transition from an image analysis algorithm to clinical feasibility trials as part of an intervention system requires integration of many components, such as imaging and tracking devices, data processing algorithms, and visualization software. The objective of our paper is to provide a freely available open-source software platform-PLUS: Public software Library for Ultrasound-to facilitate rapid prototyping of ultrasound-guided intervention systems for translational clinical research. PLUS provides a variety of methods for interventional tool pose and ultrasound image acquisition from a wide range of tracking and imaging devices, spatial and temporal calibration, volume reconstruction, simulated image generation, and recording and live streaming of the acquired data. This paper introduces PLUS, explains its functionality and architecture, and presents typical uses and performance in ultrasound-guided intervention systems. PLUS fulfills the essential requirements for the development of ultrasound-guided intervention systems and it aspires to become a widely used translational research prototyping platform. PLUS is freely available as open source software under BSD license and can be downloaded from http://www.plustoolkit.org.

/pdf/plus-open-source-toolkit-for-ultrasound-guided-intervention-1n5lehxpjm.pdf

PLUS: open-source toolkit for ultrasound-guided intervention systems.

This article is a guide to convolutional neural network technologies and their clinical applications in the analysis of radiologic images.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2018180547

Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide

Survey on deep learning for radiotherapy.

Purpose
In conventional practice of epidural needle placement, determining the interspinous level and choosing the puncture site are based on palpation of anatomical landmarks, which can be difficult with some subjects. Thereafter, the correct passage of the needle towards the epidural space is a blind “feel as you go” method. An aim-and-insert single-operator ultrasound-guided epidural needle placement is described and demonstrated.

/pdf/single-operator-real-time-ultrasound-guidance-to-aim-and-532wyyv1xl.pdf

Single-operator real-time ultrasound-guidance to aim and insert a lumbar epidural needle

Percutaneous spinal needle insertion procedures often require proper identification of the vertebral level to effectively and safely deliver analgesic agents. The current clinical method involves “blind” identification of the vertebral level through manual palpation of the spine, which has only 30% reported accuracy. Therefore, there is a need for better anatomical identification prior to needle insertion. A real-time system was developed to identify the vertebral level from a sequence of ultrasound images, following a clinical imaging protocol. The system uses a deep convolutional neural network (CNN) to classify transverse images of the lower spine. Several existing CNN architectures were implemented, utilizing transfer learning, and compared for adequacy in a real-time system. In the system, the CNN output is processed, using a novel state machine, to automatically identify vertebral levels as the transducer moves up the spine. Additionally, a graphical display was developed and integrated within 3D Slicer. Finally, an augmented reality display, projecting the level onto the patient’s back, was also designed. A small feasibility study $$(n=20)$$
 evaluated performance. The proposed CNN successfully discriminates ultrasound images of the sacrum, intervertebral gaps, and vertebral bones, achieving 88% 20-fold cross-validation accuracy. Seventeen of 20 test ultrasound scans had successful identification of all vertebral levels, processed at real-time speed (40 frames/s). A machine learning system is presented that successfully identifies lumbar vertebral levels. The small study on human subjects demonstrated real-time performance. A projection-based augmented reality display was used to show the vertebral level directly on the subject adjacent to the puncture site.

SLIDE: automatic spine level identification system using a deep convolutional neural network.

BACKGROUND: Ultrasound is receiving growing interest for improving the guidance of needle insertion in epidural anesthesia. Defining a paramedian ultrasound scanning technique would be helpful for correctly identifying the vertebral level. Finding surrogate measures of the depth of the epidural space may also improve the ease of scanning. METHODS: We examined 20 parturients with pre-epidural ultrasound in the paramedian plane, and the predicted depth was compared with the actual midline depth. The actual depth was also compared with subject biometrics, depth of transverse process, and thickness of lumbar fat. RESULTS: The scanning technique allowed the depth of the epidural space to be measured in all subjects. The depth measured in ultrasound was strongly correlated to the actual depth (R 2 = 0.8 and 95% limits of agreement of -14.8 to 5.2 mm), unlike patient biometrics (R 2 < 0.25), the depth of the neighboring transverse processes (R 2 = 0.35 and 95% limits of agreement of -13.8 to 19.1 mm), or the thickness of overlying fat (R 2 = 0.66). The duration of the ultrasound scan was 10 min at the beginning of the trial and 3 min for the last subjects. CONCLUSIONS: Paramedian ultrasound can be used to estimate the midline depth to the epidural space. The surrogate measures are not sufficiently correlated with the depth to the epidural space to recommend them as a replacement for the actual depth to the epidural space measurement.

Preinsertion Paramedian Ultrasound Guidance for Epidural Anesthesia

Accurate identification of the needle target is crucial for effective epidural anesthesia. Currently, epidural needle placement is administered by a manual technique, relying on the sense of feel, which has a significant failure rate. Moreover, misleading the needle may lead to inadequate anesthesia, post dural puncture headaches, and other potential complications. Ultrasound offers guidance to the physician for identification of the needle target, but accurate interpretation and localization remain challenges. A hybrid machine learning system is proposed to automatically localize the needle target for epidural needle placement in ultrasound images of the spine. In particular, a deep network architecture along with a feature augmentation technique is proposed for automatic identification of the anatomical landmarks of the epidural space in ultrasound images. Experimental results of the target localization on planes of 3-D as well as 2-D images have been compared against an expert sonographer. When compared with the expert annotations, the average lateral and vertical errors on the planes of 3-D test data were 1 and 0.4 mm, respectively. On 2-D test data set, an average lateral error of 1.7 mm and vertical error of 0.8 mm were acquired.

Automatic Localization of the Needle Target for Ultrasound-Guided Epidural Injections

Epidural anesthesia is the most common form of anesthesia in obstetrics. The loss-of-resistance to saline injection is used to confirm when the needle tip enters the epidural space. This procedure is highly dependent on skill and expertise, so it is useful to quantify the tissue resistance during insertion. Sensors are used to measure the force and displacement of the plunger of the syringe and the pressure at the needle tip. A model is also developed to estimate the pressure from the force and displacement. Tests are first performed on porcine tissue to compare the continuous-pressure and intermittent-pressure versions of the technique and to compare the paramedian and midline needle approaches. The accuracy of the pressure model is 12% of peak pressure for the continuous technique and 20% for the intermittent technique. Significant differences in injection flow rate were also found for the muscle, interspinous ligament, and ligamentum flavum encountered in the two approaches. A small clinical study on human subjects was performed and again significant differences were found in flow rate for different tissues. These quantitative results improve the understanding of small differences in feel that have been previously known qualitatively and may help in the development of simulators.

Victoria A. Lessoway

Papers

Single-operator real-time ultrasound-guidance to aim and insert a lumbar epidural needle

SLIDE: automatic spine level identification system using a deep convolutional neural network.

Preinsertion Paramedian Ultrasound Guidance for Epidural Anesthesia

Automatic Localization of the Needle Target for Ultrasound-Guided Epidural Injections

Instrumentation of the Loss-of-Resistance Technique for Epidural Needle Insertion