Deep 3D Convolutional Encoder Networks with Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation
Abstract of Deep 3D Convolutional Encoder Networks with Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation
The automatic segmentation of MS lesions is a very challenging task due to the large variability in lesion size, shape, intensity, and location, as well as the large variability of imaging contrasts produced by different scanners used in multi-center studies.
However, outliers are often not specific to lesions and can also be caused by intensity inhomogeneities, partial volume, imaging artifacts, and small anatomical structures such as blood vessels, which leads to the generation of false positives.
To overcome those challenges, supervised methods require large data sets that span the variability in lesion appearance and careful pre-processing to match the imaging contrast of new images with those of the training set.
Library-based approaches have shown great promise for the segmentation of MS lesions, but do not scale well to very large data sets due to the large amount of memory required to store comprehensive sample libraries and the time required to scan such libraries for matching patches.