Medical Imaging Technology

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Volume 35 (2017) Issue 4 Pages 212-226

Survey of Deep Learning Applications to Medical Image Analysis

Kenji SUZUKI

Recently, a machine learning (ML) area called deep learning emerged in the computer-vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer-vision competition, ImageNet Classification. Since then, researchers in many fields, including medical image analysis, have started actively participating in the explosively growing field of deep learning. In this paper, deep learning techniques and their applications to medical image analysis are surveyed. This survey overviewed 1) standard ML techniques in the computer-vision field, 2) what has changed in ML before and after the introduction of deep learning, 3) ML models in deep learning, and 4) applications of deep learning to medical image analysis. The comparisons between MLs before and after deep learning revealed that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is learning image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The survey of deep learning also revealed that there is a long history of deep-learning techniques in the class of ML with image input, except a new term, “deep learning”. “Deep learning” even before the term existed, namely, the class of ML with image input was applied to various problems in medical image analysis including classification between lesions and non-lesions, classification between lesion types, segmentation of lesions or organs, and detection of lesions. ML with image input including deep learning is a very powerful, versatile technology with higher performance, which can bring the current state-of-the-art performance level of medical image analysis to the next level, and it is expected that deep learning will be the mainstream technology in medical image analysis in the next few decades. “Deep learning”, or ML with image input, in medical image analysis is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical image analysis in the next few decades.

Volume 34 (2016) Issue 5 Pages 245-258

Blood Flow Contribution Analysis for Pulmonary Artery and Aorta using Contrast Enhanced Images

Tomoki SAKA, Toshiyuki GOTOH, Seiichiro KAGEI, Tae IWASAWA

The bloodstream system of the pulmonary field consists of two systems: pulmonary arteries pass and aorta pass. Until now, the influence of recirculation has been excluded by fitting a gamma function, because the recirculation from aorta is less than 10 percent of the blood flow from pulmonary arteries. In this study, we consider the way to estimate influence of recirculation by modeling the impulse response of a blood flow system that consists of 2-inputs with pulmonary arteries pass and aorta pass firstly, and we propose the way to analyze two blood flow systems separately. An experimental prototype system using these methods was applied to three cases with pulmonary tumor. Furthermore, we compared the global blood volume of the whole pulmonary field inferred from the signal intensity of the pulmonary arteries and the left atrial, with the total of the local blood volume of the whole pulmonary, by using perfusion MRI. The validity was considered by comparing the percentage of the recirculation which occupies a pulmonary field with the cardiac output volume calculated using Fast GE cine MRI.

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2021 Volume 39 Issue 3

An Introduction to Deep Learing in Image Recognition（2）: 　Configuration of GPU Environments and Application to Image Segmentation Takeshi HARA, Masahiro ODA
Explanation in Machine Learning Satoshi HARA
Training Convolutional Neural Networks with Natural Principle Hirokatsu KATAOKA, Ryosuke YAMADA, Asato MATSUMOTO
Computer Aided Diagnosis Systems Using Explainable AI for Digital Pathology Kazuki UEHARA, Hidenori SAKANASHI

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