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Segmentation is a critical step in medical image analysis. FullyConvolutional Networks (FCNs) have emerged as powerful segmentation modelsachieving state-of-the-art results in various medical image datasets. Networkarchitectures are usually designed manually for a specific segmentation task soapplying them to other medical datasets requires extensive experience and time.Moreover, the segmentation requires handling large volumetric data that resultsin big and complex architectures. Recently, methods that automatically designneural networks for medical image segmentation have been presented; however,most approaches either do not fully consider volumetric information or do notoptimize the size of the network. In this paper, we propose a novelself-adaptive 2D-3D ensemble of FCNs for medical image segmentation thatincorporates volumetric information and optimizes both the model's performanceand size. The model is composed of an ensemble of a 2D FCN that extractsintra-slice information, and a 3D FCN that exploits inter-slice information.The architectures of the 2D and 3D FCNs are automatically adapted to a medicalimage dataset using a multiobjective evolutionary based algorithm thatminimizes both the segmentation error and number of parameters in the network.The proposed 2D-3D FCN ensemble was tested on the task of prostate segmentationon the image dataset from the PROMISE12 Grand Challenge. The resulting networkis ranked in the top 10 submissions, surpassing the performance of otherautomatically-designed architectures while being considerably smaller in size.

Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation