A multistage discriminative model for tumor and lymph node detection in thoracic images.

Analysis of primary lung tumors and disease in regional lymph nodes is important for lung cancer staging, and an automated system that can detect both types of abnormalities will be helpful for clinical routine. In this paper, we present a new method to automatically detect both tumors and abnormal lymph nodes simultaneously from positron emission tomography-computed tomography thoracic images. We perform the detection in a multistage approach, by first detecting all potential abnormalities, then differentiate between tumors and lymph nodes, and finally refine the detected tumors for false positive reduction. Each stage is designed with a discriminative model based on support vector machines and conditional random fields, exploiting intensity, spatial and contextual features. The method is designed to handle a wide and complex variety of abnormal patterns found in clinical datasets, consisting of different spatial contexts of tumors and abnormal lymph nodes. We evaluated the proposed method thoroughly on clinical datasets, and encouraging results were obtained.