Electric property（EP）imaging inside the human body has attracted considerable attention as a new modality for cancer diagnosis. This paper presents the mathematical foundation on an inverse problem to reconstruct the three-dimensional distribution of the electrical conductivity and permittivity of human tissue from radiofrequency magnetic fields measured using usual magnetic resonance scanners. First, the problem is formulated with Maxwellʼs equations. The conventional methods are then introduced: i）a standard method based on the assumption that the spatial changes of the electric properties are small, and ii）a finite-element-based method with no EP restrictions. However, it is shown that these methods are sensitive to measurement noise since it is necessary to compute the second-order derivatives of the measured magnetic field data. Finally, our proposed method is introduced based on which the electric properties at arbitrary positions inside the human body can be reconstructed in a closed form in terms of the first-order derivatives and integrals of the measured magnetic field data.

This manuscript introduces our attempt to construct a multi-scale model by integrating MRI images and pathological microscope images of pancreatic cancer tumors. MRI images are useful for observing the position and shape of tumors in the body and the growth process. However, the spatial resolution is not high enough to observe the structure at the cell level. On the other hand, pathological microscopic images of tumors are useful for observing micro-anatomical structures at the cellular level inside the tumor, but they are invasive and are difficult to observe changes with respect to time in the same individual and to observe three-dimensional structures in the body. The multi-scale model of pancreatic cancer tumor is a model constructed from these MRI images and pathological microscope images, and represents the mutual statistical relationship between the macro features that can be obtained by MRI and the micro ones that can be obtained by microscope images. The outline for the construction of the multi-scale model is described in this manuscript.

In the study of multidisciplinary computational anatomy, we examined extended models of scale axis and functional axis by handling pathological images. To clarify the relationship between tissue structure/tissue function such as cancer presence and malignancy, and MRI/CT cancer images, we approach two image analysis. The first is reconstruction of 3D tissue structure using pathological images. The second is high-precision analysis of pathological images using hyperspectral. As a result, in the former, we could visualize the three-dimensional structure of 10 kinds of tissues using morphology, and in the latter, we established a method to obtain the presence of cancer from HE images with high accuracy using hyperspectral images. In addition, a basic study was conducted for optimal multi-spectral filter array for multi-band spectrum camera.

This paper describes several achievements on human embryo, pediatric, and postmortem organs done by the research group A01-2 titled “Fundamental Technologies for Integration of Multiscale Spatiotemporal Morphology in Multidisciplinary Computational Anatomy”. In particular, a spatio-temporal statistical model of landmarks on faces and brain surfaces of an embryo and that of a pediatric liver are presented. Super-resolution of an isolated lung of a postmortem body is also given in this paper.

This paper summarizes the achievements in the group A01-3 in Multidisciplinary Computational Anatomy project. We aimed to develop a subject-specific musculoskeletal model for the application in health sciences including medicine, biomechanics, and sports science. We developed multiple methods for generating a musculoskeletal model that reflects subject-specific anatomy using medical images. In this paper, we describe the four major achievements: 1) musculoskeletal segmentation using deep learning, 2) CT-MRI cross modality image synthesis, 3) 2D-3D registration of x-ray projection image and CT image for the analysis of skeletal movement, 4) estimation of muscle fiber arrangement from a clinical CT using a template derived from the high resolution cryo-section images.

In this paper, a local feature extraction method based on fast image guided filtering for organ tracking in Water-filled Laparoendoscopic Surgery (WaFLES) is presented. Robust feature extraction and matching is important for feature based tracking and 3d-reconstruction techniques. We created a non-linear scale space and extracted Hessian determinant features, described with MLDB-descriptor that performed robustly in WaFLES images, outperforming Akaze in matching. We showed that non-linear scale space methods, especially our method, are most robust for WaFLES, over de facto SIFT, SURF and ORB methods. It was demonstrated that the scale space could be created more efficiently using fast guided filtering over non-explicit diffusion. We are bringing these techniques closer to real-time capability in simpler hardware, without sacrificing performance.

Children have a high risk of pneumonia infection due to low immunity and collective life living in a group. Therefore, accurate diagnosis and early treatment are required. The purpose of this study is to develop the decision support system for thoracic diseases using chest X-ray images. As a pilot study, we propose the extraction method of the lung region using Mask R-CNN. Mask R-CNN contains the processing of object detection and semantic segmentation. In this method, 1000 images (child images; 200, adult images; 800) from the open database published by NIH were used to train Mask R-CNN. As a result of evaluation, average of Jaccard index was 93.3% and Dice index was 96.5%. Therefore, high accuracy of lung field extraction has been obtained even if various chest X-ray images of children such as lung field size exist.

This is the second in a series of three articles on “the basics of SPECT” and outlines SPECT technology from the basics to the latest. SPECT has a wide variety of devices, with scintillators and semiconductors as detector materials. SPECT systems can be classified according to number of detectors as well as types of studies. A SPECT-CT system, which is combines SPECT with CT, has also appeared.