One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been proven very challenging due to the large variation of anatomy across different patients. However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision. Due to the data driven approaches of hierarchical feature learning in deep learning frameworks, these advances can be translated to medical images without much difficulty. Several variations of deep convolutional neural networks have been successfully applied to medical images. Especially fully convolutional architectures have been proven efficient for segmentation of 3D medical images. In this article, we describe how to build a 3D fully convolutional network (FCN) that can process 3D images in order to produce automatic semantic segmentations. The model is trained and evaluated on a clinical computed tomography (CT) dataset and shows stateof-the-art performance in multi-organ segmentation.
Medicine is moving toward “personalized medicine,” which is a novel concept for cancer treatment and prevention that takes into account individual variability (patient or tumor heterogeneity) in genes, lifestyle and environment for each person. However, there are several issues in the personalized medicine such as invasive biopsy, high cost and slow throughput for examination of gene mutations. Further, since tumors are heterogeneous, a small part of a tumor obtained by a single biopsy could not be reliable for the personalized medicine, and thus it could be difficult to carry out the personalized medicine in the cancer treatment. Therefore, radiomics concept has emerged for solving the issues and performing the “practical” personalized medicine. Radiomics is a novel field, which massively and comprehensively analyzes a large amount of medical images, and extracts mineable data that can make it possible to perform the personalized medicine. In this review paper, the authors describe what radiomics is, what radiomics can do, how to perform radiomics, advantages and disadvantages of radiomics, and the future of radiomics in cancer treatment.
臓器統計モデル構築の基礎について記す．臓器統計モデルは学習用画像の集合から構築する．学習用の医用画像の集合が与えられたときに最初に必要な作業は，それら画像群の対応付けである．臓器領域を点分布モデル（point distribution model）で表現する場合には異なる患者の臓器表面間の対応付けが必要であり，レベルセットで表現する場合には体型正規化によるピクセル間の対応付けを必要とする．これら対応付けに用いられている手法群の5つのカテゴリーを記す．またその前に，deep neural network出現後の臓器統計モデルの位置づけについて簡潔に記す．