Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p < 0.05) and had an efficient implementation with a run time of 8 minutes and 3 second per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.

Purpose: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. Methods: In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. Results: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. Conclusions: The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.

Solvothermal vapor annealing at elevated temperature is applied to a thin film from a cylinder-forming polystyrene-block-poly(dimethyl siloxane) (PS-b-PDMS) diblock copolymer. At this, the film is swollen in the vapor of n-heptane (highly selective for PDMS). This vapor is stepwise replaced by the vapor of toluene (weakly selective for PS). The morphologies are investigated using in situ, real-time grazing-incidence small-angle X-ray scattering (GISAXS). The initial cylindrical morphology is transformed into, among others, the lamellar one. This novel type of experiments allows probing a trajectory in the state diagram of the PS-b-PDMS/n-heptane/toluene mixture. To corroborate the morphologies, they are generated by molecular simulations, and the 2D GISAXS maps are calculated using the distorted-wave Born approximation. To relate the morphologies to the solvent distribution in the two types of nanodomains, the latter is estimated from the intensities of the Bragg reflections in the 2D GISAXS maps along with the swelling ratio of the film. Comparison with the results from a similar experiment carried out at room temperature results in the same sequence of morphologies; however, at elevated temperature, more well-ordered structures are obtained. This new approach proves to be efficient to achieve a block copolymer thin film having a desired morphology and orientation.

CLND is fraught with considerable morbidity. Local control of the dissected nodal basins was achieved with a modified radical approach in ADs (levels I + II only) and, to a lesser extent, GDs, but not in NDs. Clinical trials are necessary to establish guidelines on the extent of lymphatic dissection.

A novel thermoresponsive gelator of (B-co-C)-b-A-b-(B-co-C) topology, comprising a poly­(2-(dimethyl­amino)­ethyl methacrylate) (PDMAEMA) weak polyelectrolyte as central block, end-capped by thermosensitive poly­(triethylene glycol methyl ether methacrylate/n-butyl methacrylate) [P­(TEGMA-co-nBuMA)] random copolymers, was designed and explored in aqueous media. The main target of this design was to control the dynamics of the stickers by temperature as to create an injectable hydrogel that behaves as a weak gel at low temperature and as a strong gel at physiological temperature. Indeed, at low temperatures, the system behaves like a viscoelastic complex fluid (dynamic network), while at higher temperatures, an elastic hydrogel is formed (“frozen” network). The viscosity increases exponentially upon heating, about 5 orders of magnitude from 5 to 45 °C, which is attributed to the exponential increase of the lifetime of the self-assembled stickers. The integration of thermo- and shear responsive properties in the gelator endows the gel with injectability. Moreover, the gel can be rapidly recovered upon cessation of the applied stress at 37 °C, simulating conditions similar to those of injection through a 28-gauge syringe needle. All these hydrogel properties render it a good candidate for potential applications in cell transplantation through injection strategies.