Hybrid Cascaded Neural Network for Liver Lesion Segmentation
Raunak Dey, Hong Yi
Abstract
Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning proper care. In this paper, we propose a cascaded system that combines both 2D and 3D convolutional neural networks to segment hepatic lesions effectively. Our 2D network operates on a slice-by-slice basis in the axial orientation to segment liver and large liver lesions; while we use a 3D network to detect small lesions that are often missed in a 2D segmentation design. We employ this algorithm on the LiTS challenge obtaining a Dice score per subject of 68.1%, which performs the best among all non-pre-trained models and the second-best among published methods. We also perform two-fold cross-validation to reveal the over- and under-segmentation issues in the annotations of the LiTS dataset.