Ultrasound image segmentation: a survey <s> • Adhoc parameters: <s> Image segmentation is the partition of an image into a set of nonoverlapping regions whose union is the entire image. The image is decomposed into meaningful parts which are uniform with respect to certain characteristics, such as gray level or texture. In this paper, we propose a methodology for evaluating medical image segmentation algorithms wherein the only information available is boundaries outlined by multiple expert observers. In this case, the results of the segmentation algorithm can be evaluated against the multiple observers' outlines. We have derived statistics to enable us to find whether the computer-generated boundaries agree with the observers' hand-outlined boundaries as much as the different observers agree with each other. We illustrate the use of this methodology by evaluating image segmentation algorithms on two different applications in ultrasound imaging. In the first application, we attempt to find the epicardial and endocardial boundaries from cardiac ultrasound images, and in the second application, our goal is to find the fetal skull and abdomen boundaries from prenatal ultrasound images. <s> BIB001 </s> Ultrasound image segmentation: a survey <s> • Adhoc parameters: <s> An algorithm devoted to the segmentation of 3-D ultrasonic data is proposed. The algorithm involves 3-D adaptive clustering based on multiparametric information: the gray-scale intensity of the echographic data, 3-D texture features calculated from the envelope data, and 3-D tissue characterization information calculated from the local frequency spectra of the radio-frequency signals. The segmentation problem is formulated as a Maximum A posterior (MAP) estimation problem. A multi-resolution implementation of the algorithm is proposed. The approach is tested on simulated data and on in vivo echocardiographic 3-D data. The results presented in the paper illustrate the robustness and the accuracy of the proposed approach for the segmentation of ultrasonic data. <s> BIB002 </s> Ultrasound image segmentation: a survey <s> • Adhoc parameters: <s> In this paper we analyze a result previously published about a comparison between two statistical tests used for evaluation of boundary detection algorithms on medical images. We conclude that the statement made by Chalana and Kim (1997) about the performance of the percentage test has a weak theoretical foundation, and according to our results, is not correct. In addition, we propose a one-sided hypothesis test for which the acceptance region can be determined in advance, as opposed to the two-sided confidence intervals proposed in the original paper, which change according to the estimated quantity. <s> BIB003 </s> Ultrasound image segmentation: a survey <s> • Adhoc parameters: <s> Characterizing the performance of image segmentation approaches has been a persistent challenge. Performance analysis is important since segmentation algorithms often have limited accuracy and precision. Interactive drawing of the desired segmentation by human raters has often been the only acceptable approach, and yet suffers from intra-rater and inter-rater variability. Automated algorithms have been sought in order to remove the variability introduced by raters, but such algorithms must be assessed to ensure they are suitable for the task. The performance of raters (human or algorithmic) generating segmentations of medical images has been difficult to quantify because of the difficulty of obtaining or estimating a known true segmentation for clinical data. Although physical and digital phantoms can be constructed for which ground truth is known or readily estimated, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms which reproduce the full range of imaging characteristics and normal and pathological anatomical variability observed in clinical data. Comparison to a collection of segmentations by raters is an attractive alternative since it can be carried out directly on the relevant clinical imaging data. However, the most appropriate measure or set of measures with which to compare such segmentations has not been clarified and several measures are used in practice. We present here an expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE). The algorithm considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation. The source of each segmentation in the collection may be an appropriately trained human rater or raters, or may be an automated segmentation algorithm. The probabilistic estimate of the true segmentation is formed by estimating an optimal combination of the segmentations, weighting each segmentation depending upon the estimated performance level, and incorporating a prior model for the spatial distribution of structures being segmented as well as spatial homogeneity constraints. STAPLE is straightforward to apply to clinical imaging data, it readily enables assessment of the performance of an automated image segmentation algorithm, and enables direct comparison of human rater and algorithm performance. <s> BIB004 </s> Ultrasound image segmentation: a survey <s> • Adhoc parameters: <s> This paper describes a new method for segmentation of fetal anatomic structures from echographic images. More specifically, we estimate and measure the contours of the femur and of cranial cross-sections of fetal bodies, which can thus be automatically measured. Contour estimation is formulated as a statistical estimation problem, where both the contour and the observation model parameters are unknown. The observation model (or likelihood function) relates, in probabilistic terms, the observed image with the underlying contour. This likelihood function is derived from a region-based statistical image model. The contour and the observation model parameters are estimated according to the maximum likelihood (ML) criterion, via deterministic iterative algorithms. Experiments reported in the paper, using synthetic and real images, testify for the adequacy and good performance of the proposed approach. <s> BIB005 </s> Ultrasound image segmentation: a survey <s> • Adhoc parameters: <s> We propose a new spatio-temporal elastic registration algorithm for motion reconstruction from a series of images. The specific application is to estimate displacement fields from two-dimensional ultrasound sequences of the heart. The basic idea is to find a spatio-temporal deformation field that effectively compensates for the motion by minimizing a difference with respect to a reference frame. The key feature of our method is the use of a semi-local spatio-temporal parametric model for the deformation using splines, and the reformulation of the registration task as a global optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy to obtain a higher speed and robustness. We evaluated the accuracy of our algorithm using a synthetic sequence generated with an ultrasound simulation package, together with a realistic cardiac motion model. We compared our new global multiframe approach with a previous method based on pairwise registration of consecutive frames to demonstrate the benefits of introducing temporal consistency. Finally, we applied the algorithm to the regional analysis of the left ventricle. Displacement and strain parameters were evaluated showing significant differences between the normal and pathological segments, thereby illustrating the clinical applicability of our method. <s> BIB006 </s> Ultrasound image segmentation: a survey <s> • Adhoc parameters: <s> Despite being a valuable tool for volume measurement and the analysis of complex geometry, the need for an external position sensor is holding up the clinical exploitation of freehand 3-D ultrasound. Some sensorless systems have been developed, using speckle decorrelation for out-of-plane distance estimation, but their accuracy is still not as good as that of sensor-based systems. Here, we examine the widely held belief that accuracy can be improved by limiting the distance measurements to patches of ultrasound data containing fully developed speckle. Without speckle detection, we observe that scan separation is systematically underestimated by 33.1% in biological tissue. We describe a number of speckle detectors and show that they reduce the underestimate to about 25%. We conclude that speckle classification can improve the quality of distance estimation, but not sufficiently to achieve accurate, metric reconstruction of the insonified volume. <s> BIB007
none or sensitivity analysis done, yes and some attempt at sensitivity analysis, yes and no sensitivity done. • Automation: full, interactive guidance/correction, substantial guidance. • In general, to validate medical image segmentation methods quantitatively, one can use simulation, phantom studies, animal model studies, and clinical studies using manual delineation, another modality as a "gold standard" or reference, or some correlation with clinical outcome. The literature on ultrasound segmentation indicates that some simulation and phantom studies have been performed but predominantly clinical data is used in validation. This partly reflects the difficulty in defining realistic simulations and phantoms. An effective ultrasound simulation package, "Field II" is available  ,  . 8 The package provides an excellent framework to simulate ultrasound transducer fields and ul-trasound imaging using linear acoustics. The program has been used in number of application to provide ground truth data (see e.g., BIB005 and BIB002 BIB006 BIB007 ). • Manual delineation on clinical data is by far the most popular means of performance assessment although depending on the clinical area, there can be significant inter-expert and intra-expert variability (quite often, simply because manual segmentation is not a task that they would normally do) and delineation can be difficult to do. Therefore, one needs to take care in interpreting results when manual delineation is used as the reference. • There is also a general lack of standardization of performance measures, which makes it difficult to directly compare methods. The one exception here is in cardiology, where the method of Chalana and Kim BIB001 has been used by a number of groups (however, see BIB003 for a correction of the percent statistic test). This has the advantage that multiple experts are used to define the reference. The method of Chalana and Kim assumes that segmentation boundaries can be well-defined. This is not the case, for example, for malignant breast masses where a region-based metric is more applicable. Performance metrics used in breast ultrasound segmentation performance assessment have employed quite simple area-based measures with comparison with a single expert or multiple experts treated separately. This area might benefit from moving towards using performance methods that use multiple expert segmentations such as the simultaneous truth and performance level estimation (STAPLE) algorithm BIB004 . • From the tables we note the low number of papers reported with validation on databases with more than 50 cases. • There are also no standard databases on which different groups can compare methods, so comparisons have only been made between methods developed by individual groups. This is problematic for the field as a whole, as clinical ultrasound image quality varies a lot, more so than in other areas of medical imaging such as CT and MRI.