KEYWORDS: Tumors, Magnetic resonance imaging, Image registration, 3D image processing, 3D modeling, 3D image reconstruction, Pancreatic cancer, 3D image enhancement, Neural networks, Spatial resolution
In this paper, we report on the construction of a pancreatic tumor model that represents the relationship between the tumor growth and the micro anatomical structures. The former, the tumor growth, is described by referring to the temporal series of MRI images of the whole body and the latter, the micro structures of the tumor, is described by a spatial series of microscopic images of thin-sections sliced from the extracted pancreatic tumor. For the model construction, we developed new non-rigid registration methods for (1) accurate description of tumor growth, (2) reconstruction of 3D microscopic images, and (3) registration between an MRI image and corresponding microscopic images. In addition, we constructed a neural network that can generate a set of fake microscopic image patches of a pancreatic tumor that corresponds to each voxel inside the tumor region in an MRI image. The outlines of the methods are introduced and some examples of experimental results are demonstrated.
Given microscope images, one can observe 2D cross-sections of 3D micro anatomical structures with high spatial resolutions. Each of the 2D microscope images alone is, though, not suitable for studying the 3D anatomical structures and hence many works have been done on a 3D image reconstruction from a given series of microscope images of histological sections obtained from a single target tissue. For the 3D image reconstruction, an image registration technique is necessary because there exists the independent translation, rotation, and non-rigid deformation of the histological sections. In this paper, a landmark-based method of fully non-rigid image registration for the 3D image reconstruction is proposed. The proposed method first detects landmarks corresponded between given images by using a template matching and then non-rigidly deforms the images so that the corresponding landmarks detected in different images are located along a single smooth curve in the reconstructed 3D image. Most of all conventional methods for the reconstruction of 3D microscope image registers two consecutive images at a time and many micro anatomical structures often have unnatural straight shape along the vertical (z) direction in the resultant 3D image because, roughly speaking, the conventional methods registers two given images so that pixels with the same coordinates in the two images have the same pixel value. The proposed method, on the other hand, determine the deformations of all given images by referring to the all images and deforms them simultaneously. In the experiments, a 3D microscope image of the pancreas of a KPC mouse was reconstructed from a series of microscope images of the histological sections.
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