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Image segmentation for 3D printing and 3D visualization has become an essential component in many fields of medical research, teaching, and clinical practice. Medical image segmentation requires sophisticated computerized quantifications and visualization tools. Recently, with the development of artificial intelligence (AI) technology, tumors or organs can be quickly and accurately detected and automatically contoured from medical images. This paper introduces a platform-independent, multi-modality image registration, segmentation, and 3D visualization program, named artificial intelligence-based medical image segmentation for 3D printing and naked eye 3D visualization (AIMIS3D). YOLOV3 algorithm was used to recognize prostate organ from T2-weighted MRI images with proper training. Prostate cancer and bladder cancer were segmented based on U-net from MRI images. CT images of osteosarcoma were loaded into the platform for the segmentation of lumbar spine, osteosarcoma, vessels, and local nerves for 3D printing. Breast displacement during each radiation therapy was quantitatively evaluated by automatically identifying the position of the 3D printed plastic breast bra. Brain vessel from multi-modality MRI images was segmented by using model-based transfer learning for 3D printing and naked eye 3D visualization in AIMIS3D platform. |
