The invention discloses a pulmonary nodule segmentation method based on average intensity projection and a translation gaussian model. According to the pulmonary nodule segmentation method, the problems that angiosynizesis type pulmonary nodules are difficult to segment, and have low segmentation accuracy, high false positive rate and large data processing amount are mainly solved. The pulmonary nodule segmentation method comprises the following implementation steps of: performing sparse sampling on original CT data by using an AIP (average intensity projection) technology; performing pulmonary parenchymal extraction, threshold segmentation, morphology processing and geometric feature coarse extraction on an AIP image, and determining rough outlines of the nodules; and building the translation gaussian model to precisely segment the pulmonary nodules. According to the pulmonary nodule segmentation method, the angiosynizesis type pulmonary nodules can be extracted, and the false positive can be effectively restrained, and the method has relatively high robustness. Compared with the conventional method, the pulmonary nodule segmentation method has the advantages that: the segmentation accuracy is higher, the data processing amount is effectively reduced, and batch processing can be realized.