Magnetic resonance images of brain tumors are routinely used in neuro-oncology clinics for diagnosis, treatment planning, and post-treatment tumor surveillance. Currently, physicians spend considerable time manually delineating different structures of the brain. Spatial and structural variations, as well as intensity inhomogeneity across images, make the problem of computer-assisted segmentation very challenging. We propose a new image segmentation framework for tumor delineation that benefits from two state-of-the-art machine learning architectures in computer vision, i.e., Inception modules and U-Net image segmentation architecture. Furthermore, our framework includes two learning regimes, i.e., learning to segment intra-tumoral structures (necrotic and non-enhancing tumor core, peritumoral edema, and enhancing tumor) or learning to segment glioma sub-regions (whole tumor, tumor core, and enhancing tumor). These learning regimes are incorporated into a newly proposed loss function which is based on the Dice similarity coefficient (DSC). In our experiments, we quantified the impact of introducing the Inception modules in the U-Net architecture, as well as, changing the objective function for the learning algorithm from segmenting the intra-tumoral structures to glioma sub-regions. We found that incorporating Inception modules significantly improved the segmentation performance ( p < 0.001) for all glioma sub-regions. Moreover, in architectures with Inception modules, the models trained with the learning objective of segmenting the intra-tumoral structures outperformed the models trained with the objective of segmenting the glioma sub-regions for the whole tumor ( p < 0.001). The improved performance is linked to multiscale features extracted by newly introduced Inception module and the modified loss function based on the DSC.

Elephas is an extension of Keras, which allows you to run distributed deep learning models at scale with Apache Spark. It was built to allow researchers and developers to distribute their deep learning experiments as easily as possible on a Spark computer cluster. With elephas, researchers can currently run data-parallel training of deep learning models with distribution modes as suggested in (Dean et al., 2012), (Recht et al., 2011) and (Noel & Osindero, 2014. Additionally, elephas supports distributed training of ensemble models. Until version 2.1., elephas also supported distributed hyper-parameter optimization of Keras models.Elephas keeps the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models. When ready, researchers can then scale out their experiments on massive data sets. Elephas comes with full API documentation and examples to get you started. Initiated in late 2015, elephas has been actively maintained since then and has reached maturity for distributed deep learning on Spark.

No abstract