Jing Yuan

TL;DR: Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, it is shown that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained.

TL;DR: HyperDenseNet is proposed, a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems and has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation.

TL;DR: It is proved that the proposed continuous max-flow and min-cut models, with or without supervised constraints, give rise to a series of global binary solutions λ∗(x) ∊ {0,1}, which globally solves the original nonconvex image partitioning problems.

TL;DR: Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden.

TL;DR: This work proposes a novel convex formulation with a continous 'max-flow' functional of the Potts model, which avoids extra computational load in enforcing the simplex constraints and naturally allows parallel computations over different labels.