Weakly Supervised Learning of Recurrent Residual ConvNets for Pancreas Segmentation in CT Scans

Huiru Zeng, Xiaohua Hu, Leiting Chen, Chuan Zhou, Yang Wen

Abstract: Deep neural networks trained by medical images with dense annotations have revealed favourable performance on accurate organ segmentation. The current supervised methods demand voxel-level annotations which are not easily accessible due to the consuming of time and requirements of specialized knowledge and skills. In this paper, we propose a weakly supervised method based on a recurrent residual convolutional neural network trained only with image-level labels to generate voxel-level segmentation. The recurrent residual convolutional units take advantage of contextual information of successive slices and a spatial pooling layer is introduced after the last convolutional layer to aggregate local features and learn accurate localization. The final segmentation mask is computed by applying a conditional random field for spatial prediction. Our method shows competitive performance to fully supervised methods on the public NIH-CT-82 dataset for pancreas segmentation.