Most fully automatic segmentation approaches target a single anatomical structure in a specific combination of image modalities and are often difficult to extend to other modalities and protocols or segmentation tasks. More recently, deep learning-based approaches promise to be readily adaptable to new applications as long as a suitable training set is available, although most deep learning architectures are still tuned towards a specific application and data domain. In this paper, we propose a novel fully convolutional neural network architecture for image segmentation and show that the same architecture with the same learning parameters can be used to train models for 20 different organs on two different protocols, while still achieving segmentation accuracy that is on par with the state-of-the-art. In addition, the architecture was designed to minimize the amount of GPU memory required for processing large images, which facilitates the application to full-resolution whole-body CT scans. We have evaluated our method on the publicly available data set of the VISCERAL multi-organ segmentation challenge and compared the performance of our method with those of the challenge and two recently proposed deep learning-based approaches. We achieved the highest Dice similarity coefficients for 17 out of 20 organs for the contrast enhanced CT scans and for 10 out of 20 organs for the uncontrasted CT scans in a cross-comparison between our method and participating methods.
Tom Brosch and Axel Saalbach, "Foveal fully convolutional nets for multi-organ segmentation," Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740U (Presented at SPIE Medical Imaging: February 12, 2018; Published: 2 March 2018); https://doi.org/10.1117/12.2293528.
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