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An assessment of genetic improvement in turf‐type perennial ryegrass was performed at a network of six locations. A comparison was made of the turf performances of five natural populations, five forage‐type cultivars used for turf seeding until the 1980s and 31 turf‐type cultivars released from 1974 to 2004. Populations and cultivars were also compared in two spaced‐plant experiments and in two seed‐yield trials. Trait regressions on registration year of turf‐type cultivars showed that breeding had been highly successful in improving the turf aesthetic merit (from +8·8 to +12·5% per decade according to seasons), wear tolerance (+5·4% per decade) and crown‐rust resistance (+8·9% per decade) and in lessening the turf height increase rate (−0·43 mm day−1 per decade). Turf winter greenness had been marginally improved, whereas summer greenness and seed yield had not been significantly changed. A multivariate analysis provided evidence that turf density and fineness played a major role in the visual assessment of turf aesthetic merit and that wear tolerance was closely associated with turf density. Conflicting trait associations may have precluded improvements in turf ground‐cover 3 months after sowing, turf winter greenness and turf persistency.

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Purpose: The automatic segmentation of multiple sclerosis lesions in magnetic resonance imaging has the potential to reduce radiologists' efforts on a daily time-consuming task and to bring more reproducibility. Almost all new segmentation techniques make use of convolutional neural networks, with their own different architecture. Architectural choices are rarely explained. We aimed at presenting the relevance of a U-net like architecture for our specific task and at building an efficient and simple model. Approach: An experimental study was performed by observing the impact of applying different mutations and deletions to a simple U-net like architecture. Results: The power of the U-net architecture is explained by the joint benefits of using an encoderdecoder architecture and by linking them with long skip connections. Augmenting the number of convolutional layers and decreasing the number of feature maps allowed us to build an exceptionally light and competitive architecture, the MPU-net, with only approximately 30,000 parameters. Conclusion:The empirical study of the U-net has led to a better understanding of its architecture. It has guided the building of the MPU-net, a model far less parameterized than others (at least by a factor of seven). This neural network achieves a human level segmentation of multiple sclerosis lesions on FLAIR images only. It shows that this segmentation task does not necessitate overly complicated models to be achieved. This gives the opportunity to build more explainable models which can help such methods to be adopted in a clinical environment.

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