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Segmentation of anatomical structures, from modalities like computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound, is a key enabling technology for medical applications such as diagnostics, planning and guidance. More efficient implementations are necessary, as most segmentation methods are computationally expensive, and the amount of medical imaging data is growing. The increased programmability of graphic processing units (GPUs) in recent years have enabled their use in several areas. GPUs can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. Furthermore, using a GPU enables concurrent visualization and interactive segmentation, where the user can help the algorithm to achieve a satisfactory result. This review investigates the use of GPUs to accelerate medical image segmentation methods. A set of criteria for efficient use of GPUs are defined and each segmentation method is rated accordingly. In addition, references to relevant GPU implementations and insight into GPU optimization are provided and discussed. The review concludes that most segmentation methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.

In recent years there has been a considerable improvement in the quality of ultrasound (US) imaging. The integration of 3D US with neuronavigation technology has created an efficient and inexpensive tool for intra-operative imaging in neurosurgery. In this review we present the technological background and an overview of the wide range of different applications. The technology has so far mostly been applied to improve surgery of tumours in brain tissue, but it has also been found to be useful in other procedures such as operations for cavernous haemangiomas, skull base tumours, syringomyelia, medulla tumours, aneurysms, AVMs and endoscopy guidance.

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BackgroundIntraoperative ultrasound imaging is used in brain tumor surgery to identify tumor remnants. The ultrasound images may in some cases be more difficult to interpret in the later stages of the operation than in the beginning of the operation. The aim of this paper is to explain the causes of surgically induced ultrasound artefacts and how they can be recognized and reduced.MethodsThe theoretical reasons for artefacts are addressed and the impact of surgery is discussed. Different setups for ultrasound acquisition and different acoustic coupling fluids to fill up the resection cavity are evaluated with respect to improved image quality.ResultsThe enhancement artefact caused by differences in attenuation of the resection cavity fluid and the surrounding brain is the most dominating surgically induced ultrasound artefact. The influence of the artefact may be reduced by inserting ultrasound probes with small footprint into the resection cavity for a close-up view of the areas with suspected tumor remnants. A novel acoustic coupling fluid developed for use during ultrasound imaging in brain tumor surgery has the potential to reduce surgically induced ultrasound artefacts to a minimum.ConclusionsSurgeons should be aware of artefacts in ultrasound images that may occur during brain tumor surgery. Techniques to identify and reduce image artefacts are useful and should be known to users of ultrasound in brain tumor surgery.

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

Three-dimensional (3-D) ultrasound (US) is an emerging new technology with numerous clinical applications. Ultrasound probe calibration is an obligatory step to build 3-D volumes from 2-D images acquired in a freehand US system. The role of calibration is to find the mathematical transformation that converts the 2-D coordinates of pixels in the US image into 3-D coordinates in the frame of reference of a position sensor attached to the US probe. This article is a comprehensive review of what has been published in the field of US probe calibration for 3-D US. The article covers the topics of tracking technologies, US image acquisition, phantom design, speed of sound issues, feature extraction, least-squares minimization, temporal calibration, calibration evaluation techniques and phantom comparisons. The calibration phantoms and methods have also been classified in tables to give a better overview of the existing methods.