The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method

Fusogenic Metallosupramolecular Brush Vesicles

The electrostatic combination of a cationic metallosupramolecular polyelectrolyte (Fe-MSP) with sulfonate-terminated polymers leads to the formation of metallosupramolecular brushes (MSBs). The resulting MSBs can self-assemble into vesicular structures in chloroform/methanol (v/v = 1:1) mixture solvents. The rigid-rod Fe-MSP chain has to bend for the formation of the vesicles, which accompanies the presence of a lateral tension and thus induces a spontaneous vesicle fusion with an hour-scale fusion time. For this much longer fusion process, the arrow-like protrusion, stalk-like intermediate, and hemifusion diaphragm are clearly observed by transmission electron microscopy. The complete fusion into larger vesicles significantly releases the lateral tension