In the context of cardiac viability assessment, we propose a new fully automatic method to segment and quantify myocardial pathological tissues in Late Enhancement Cardiac Magnetic Resonance images. Our two main contributions are a generic image intensity analysis and an original variational segmentation method, the Fast Region Competition. The obtained results are robust to anatomical variability and partial volume effects and false positives are avoided. To validate our results, we use representations that are independent of myocardium shape and size and compute clinically relevant indicators. The proposed method was tested on 100 slices and compared to other classical segmentation approaches, showing the best agreement with semi-automatic expert delineations.

Objective To evaluate whether an automated tool can recognize a structure of interest and measure fetal head circumference (HC), abdominal circumference (AC) and femur length (FL) on frozen two‐dimensional ultrasound images. Methods Ultrasound examinations were performed in 100 singleton pregnancies between 20 and 40 weeks of gestation, ensuring an even distribution throughout gestational age. In each pregnancy, three standard biometric variables (HC, AC, FL) were measured each in three different images obtained for this purpose (i.e. nine independent image acquisitions). An algorithm (Philips Research) was used to detect the structure of interest and automatically place calipers for measurement. Caliper placement was assessed in two ways. First, subjective clinical assessment was performed to determine whether the caliper placement was correct, and caliper position was classified as ‘acceptable for clinical use’, ‘minor adjustment required’ or ‘major adjustment required’. Second, the resulting automatic measurements were compared with manual measurements, taken in real time. Mean difference errors were calculated and expressed as percentages to correct for fetal growth with advancing gestation. Results After exclusion of one pregnancy due to missing images, a total of 891 images (297 for each biometric variable) from 99 pregnancies were analyzed. The algorithm failed to place calipers for the AC in nine images, whereas there were no failures in caliper placement for measurement of HC and FL. On subjective quality assessment of automatic caliper placement, in 475 (53.3%) images position of the calipers was judged to be clinically acceptable and did not require any adjustment, while in 317 (35.6%) and 90 images (10.1%) minor and major adjustments were required, respectively. The mean error between manual and automatic measurement of HC was −0.21 cm corresponding to a percentage error of −0.81% with 95% limits of agreement (LOA) between −3.73% and 2.12%. For AC and FL measurements, the mean error was, respectively, 0.72 cm (percentage error, 2.40%) with LOA between −9.48% and 14.27%, and 0.21 cm (percentage error, 3.76%) with LOA between −8.38% and 15.91%. Conclusions The automated tool identified correctly the biometric variable of interest in 99% of frozen images. The resulting measurements had a high degree of accuracy and compared well with previously published manual‐to‐manual agreement. The measurements exhibited bias, with the automated tool underestimating biometry; this could be overcome by further improvements in the algorithm. Nevertheless, adjustable calipers for manual correction remains a requirement. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

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3D contrast-enhanced ultrasound (CEUS) is a powerful imaging technique for tumour vascularity assessment, which is critical for radio-frequency ablation (RFA) planning or for the assessment of response to antiangiogenic therapies. In this paper, we propose a novel semi-automated method for the quantification of tumour vascularity in 3D CEUS data. We apply a two-step framework combining an interactive segmentation of the tumour necrosis followed by an automatic detection of the vascularity based on implicit representations. Experimental results on 3D CEUS images of renal cell carcinomas (RCC) show that our method is promising in terms of speed and quality.

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