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One of the key problems of computer-aided diagnosis is to segment specific anatomy structures in tomographic images as fast and accurately as possible, which is an important step toward identifying pathologically changed tissues. The segmentation accuracy has a significant impact on diseases diagnosis as well as the therapeutic efficacy. This paper presents a fast and robust weak-supervised pulmonary nodule segmentation method based on a modified self-adaptive FCM algorithm. To improve the traditional FCM, we firstly introduce an enhanced objective function, which computes the membership value according to both the grayscale similarity and spatial similarity between central pixels and neighbors. Then, a probability relation matrix between clusters and categories is constructed by using a small amount of prior knowledge learned from training samples. Based on this matrix, we realize a weak …