Joint Registration and Segmentation of Serial Lung CT Images in Microendoscopy Molecular Image-Guided Therapy

  • Authors:

  • Zhong Xue;Kelvin Wong;Stephen Wong

  • Affiliations:

  • Department of Radiology, The Methodist Hospital and The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, USA;Department of Radiology, The Methodist Hospital and The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, USA;Department of Radiology, The Methodist Hospital and The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, USA

  • Venue:
  • MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
  • Year:
  • 2008

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Abstract

In lung cancer image-guided therapy, a real-time electromagnetic tracked microendoscopic optical imaging probe is guided to the small lung lesion of interest. The alignment of the pre-operative lung CT images as well as the intra-operative serial images is often an important step to accurately guide and monitor the interventional procedure in the diagnosis and treatment of these small lung lesions. Registering the serial images often relies on correct segmentation of the images and on the other hand, the segmentation results can be further improved by temporal alignment of the serial images. This paper presents a joint serial image registration and segmentation algorithm. In this algorithm, serial images are segmented based on the current deformations, and the deformations among the serial images are iteratively refined based on the updated segmentation results. No temporal smoothness about the deformation fields is enforced so that the algorithm can tolerate larger or discontinuous temporal changes that often appear during image-guided therapy. Physical procedure models could also be incorporated to our algorithm to better handle the temporal changes of the serial images during intervention. In experiments, we apply the proposed algorithm to align serial lung CT images. Results using both simulated and clinical images show that the new algorithm is more robust compared to the method that only uses deformable registration.