The most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted by the challengers except one demonstrated good performance with median accuracy scores above 0.8.

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Tumour functional sphericity was found to be dependent on the segmentation method, although the accuracy in retrieving the true sphericity was not dependent on tumour volume. In addition, even accurate segmentation can lead to an inaccurate sphericity value, and vice versa. Sphericity had similar or lower prognostic value than volume alone in the patients with lung cancer, except when determined using the FLAB method for which there was a small improvement in stratification when the parameters were combined.

Class I hybrid poly(N-isopropylacrylamide)/silica hydrogels, PNIPAM/SiO2, were prepared by a new one shot synthesis. In this approach, the free-radical polymerization of vinyl groups of N-isopropylacrylamide (NIPAM) and the hydrolysis-condensation of alkoxy groups of tetramethoxysilane (TMOS) are performed concomitantly using sodium persulfate and 3-(dimethylamino)-propionitrile, a well-known couple to initiate the organic polymerization. The cross-linker is N,N-methylenebisacrylamide. The kinetic study of mechanical properties from the sol-to-gel state for different ratios of TMOS/NIPAM was investigated by rheological ultrasonic measurements. The thermoresponse of hybrid materials was investigated by differential scanning calorimetry and the measurements showed that hybrid gels present a lower critical solution temperature, which is similar with one of single organic hydrogel.

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Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multitask medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https:// learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra-and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new stateof-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.