3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning. (October 2019) - Daoud, Bilel; Morooka, Ken'ichi; Kurazume, Ryo; Leila, Farhat; Mnejja, Wafa; Daoud, Jamel
Highlights: We propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) from axial, coronal and sagittal CT images. The proposed method introduces a cascade strategy: (1) detecting and eliminating non-target organ regions from a given CT image. (2) Extracting NPC from the remained regions in the CT image. The proposed method uses as its input several types of overlapping patches with different sizes. From the experimental results using our database composed of 70 NPC patients, the proposed system archives the best performance for detecting NPC compared with conventional methods for detecting NPC directly from CT images. Abstract: In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images, there are organs, called non-target organs, which NPC never invades. Therefore, the first phase is to detect and eliminate non-target organ regions from the CT images. In the second phase, NPC is extracted from the remained regions in the CT images. Convolutional neural networks (CNNs) are applied to detect non-target organs and NPCs. The proposed system determines the final NPC segmentation by integrating three results obtained from coronal, axial and sagittal images. Moreover, we construct two CNN-based NPC detection systems using one kind of overlapping patches with a fixed size and various overlapping patches with different sizes. From the experiments using CT images of 70 NPC patients, our proposed systems, especially the system using various patches, achieves the best performance for detecting NPC compared with conventional NPC detection methods.