This paper presents spatiotemporal statistical models of organ surfaces during human embryonic development, in which size, shape, and topology of organs are dynamically changed. The modeling scheme comprised two steps: (1) each temporal stage of an embryo was statistically modeled, and (2) models between neighboring temporal stages were interpolated. This paper includes optimization of interpolation techniques and a novel method for modeling nested shapes, such as brain and ventricular surfaces. The effectiveness of our method was demonstrated in the context of statistical modeling of a human embryo from the Kyoto Collection.
Histopathological imaging and Magnetic Resonance (MR) are two equally important yet very distinct modalities of medical imaging. The high resolution of the first and the non-invasiveness of the later provide complementary information for medical diagnosis and research. Due to their largely different resolutions, the registration between 3D images of these two modalities is challenging. The objective of this paper is to create a multimodal 3D model of pancreatic cancer tumor by performing the registration of a reconstructed 3D pathological image and an MR image from a KPC mouse. The tumor portions were manually segmented and the 3D pathological image was reconstructed using landmark-based non-linear registration. The process starts by registering the outline of the images using the LDDMM non-linear registration method to match the binary labels of the tumor regions. Next, a non-linear B-spline deformation method based on mutual information maximization is used to register the internal structures of the images. Experimental results show that the overall shape of the tumor and its internal necrosis portion could be correctly registered, although the quality of the manual segmentations affects the accuracy of the registration.
Proc. SPIE. 11050, International Forum on Medical Imaging in Asia 2019
KEYWORDS: 3D image enhancement, 3D image reconstruction, Tumors, Magnetic resonance imaging, 3D modeling, Image registration, Neural networks, Spatial resolution, Pancreatic cancer, 3D image processing
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.
The goal of our research is to describe the growth of a tumor region captured in a temporal series of MRI images. The tumor growth description requires registration of the given images in order to compensate the body deformation. It should be noted that one can apply an existing non-rigid registration technique for the compensation but it would deform the tumor region in a source image and the resultant description of the tumor growth would be inaccurate. We hence developed a Large Deformation Diffeomorphic Mapping (LDDMM) method that can non-rigidly registers given images while keeping the shape of the tumor region in a source image. The proposed method was applied to a series of MRI images of a KPC mouse and the results showed our method could successfully described the tumor growth.
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.
We propose a visual tracking system that uses RFID-tags to identify
objects. The system firstly identifies an object in front of the
camera, and pulls up data of the object from the database. The data
includes a cad model of the object that is used for estimating 3D
motion relative to the camera and a set of image features that is used
for detecting the object in the initial image. The set of image
features is generated based on the cad model by means of the AdaBoost
algorithm and distinguishes the object in images from the backgrounds
efficiently. Identifying the object, the system processes images using
models that are specialized in the object in front of the camera.