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3D Tumor Localization and Reconstruction in Bioluminescence Images

This work introduces a novel and efficient algorithm for reconstructing the 3D shapes of tumors from a set of 2D bioluminescence images which are taken by the same camera but after continually rotating the animal by a small angle. The method is efficient and robust enough to be used for analyzing
the repeated imaging of a same animal transplanted with gene marked cells. There are several steps in our algorithm. First, the silhouettes (or boundaries) of the animal and its interior hot spots (corresponding to tumors) are segmented in the set of bioluminescence images. Second, the images are registered according to the projection of the animal rotating axis. Third, the images are mapped onto 3D projection planes and from the viewpoint of each plane, the visual hulls of the animal and its interior tumors are reconstructed. Then, the intersection of visual hulls from all viewpoints approximates the shape of the
animal and its interior tumors. In order to visualize in 3D the structure of the tumor, we also co-register the BLI-reconstructed crude structure with detailed anatomical structure extracted from high-resolution micro-CT on a single platform. The experimental results show promising performance of our reconstruction and co-registration method.


.Papers

 


Bioluminense Images

  • An animal is injected with tumor cells expressing Luciferase. Tumors are formed in the animal, and the animal can be imaged by BLI (e.g. on day 12 after injection) following the injection of D-luciferin given intraperitoneally. The tumor cells produce light and show high response in BLI images.

  • An animal (mouse) was injected with 20,000 tumor cells expressing luciferase Tumors were formed in the abdomen, and the animal was imaged on day 12 after injection following injection of 150 mg/kg D-luciferin given intraperitoneally.

  • The animal was anesthetized with isoflurane inhalation, and immobilized with a cylindrical 50ml tube that can be rotated by a small angle at a time from the vertical axis.

  • The animal was then placed on the image stage of the IVIS 100 machine (Xenogen, Alameda, CA) and BLI images were acquired when rotating the tube.

  • The same animal was carried over to the microCT machine in the same position while remaining under isoflurane anesthesia.

 

Samples

  • Using a series of BLI images taken by the same camera but after continually rotating the animal by a small angle.

  • Instead of using multiple cameras, we use a single CCD camera setup which is readily available in commercial BLI imaging systems (e.g. IVIS 200 imaging station).

  • We can acquire any number of images by adjusting the rotation angle; small angle insures good correspondence points between consecutive images.

 

Tumor Localization, Segmentation, Reconstruction

  • We compute the feature correspondences between consecutive images to reconstruct the 3D locations of feature points

  • Reconstruct 3D structures and tumor depth by shooting rays from corresponding feature points and computing 3D ray intersection. Setting up 3D projection plane geometry

  • Segment tumor (high response) area in every BLI image. Project cylindrical visual hull based on the tumor areas

 

Registration bewtween Bioluminense Images and CT images

  • Register CT crude structure with structure reconstructed from BLI using shape registration algorithm

 

Final Visualization Results

  • Recovered tumor shape and location and visualization after co-registration with microCT

  • Extract mouse surface and skeleton structure from microCT following standard CT segmentation.

  • Register CT crude structure with structure reconstructed from BLI using shape registration algorithm