Intelligent Tumor Synthesis Based on Medical Image Knowledge for Liver Tumor Segmentation

Accurate segmentation of liver tumors is crucial for their proper diagnosis and treatment. However, achieving high levels of precision typically depends on meticulous manual annotation, a process that is not only labor-intensive but also constrained by the scarcity of large-scale, real-world datasets. These datasets are indispensable for the training and validation of segmentation algorithms. Furthermore, the liver’s considerable variation in size, shape, and pathology type poses a challenge in collecting a sufficient number of image samples that adequately represent this diversity. To surmount the challenges of the time-consuming manual annotation process and the limitations in data acquisition, there is an urgent need for the development of an efficient and comprehensive tumor generation method. Some existing approaches, such as those utilizing Gaussian blurred ellipses to simulate tumors, fail to accurately reflect the biological complexity and pathological diversity of liver tumors. In this paper, we introduce an innovative tumor synthesis method LDMP (Latent Diffusion Model for Pathology) that leverages medical imaging knowledge to more accurately replicate the intricacies of liver tumor morphology and pathology. This approach aims to enhance the quality and diversity of training data, thereby improving the performance of segmentation algorithms and ultimately contributing to more precise diagnoses and treatments. The method uses deep learning techniques, particularly diffusion models, to simulate real liver CT images and incorporates the biological properties of tumors into the synthesis process to generate realistic tumor images. The quality of the synthetic images is assessed using principal component analysis (PCA) and Kullback-Leibler divergence (KL) to ensure the authenticity of the tumor’s spatial structure. Experimental results show that the proposed method can significantly improve the Dice Similarity Coefficient (DSC) of the tumor segmentation model and enable researchers to freely define the size and blur degree of the tumor, thereby creating medical images with precise annotations. In addition, we introduce a self-checking step before the output of synthetic data, which provides a new paradigm in the field of image synthesis and effectively compensates for potential errors in synthetic data. Our approach not only provides an effective solution for medical image analysis but also provides high-quality synthetic image resources for medical education and clinical practice. Codes are available at: https://github.com/jhf0721/tumor