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Review
. 2012 Jul;16(5):933-51.
doi: 10.1016/j.media.2012.02.005. Epub 2012 Feb 23.

Machine learning and radiology

Shijun Wang  1 , Ronald M Summers
Affiliations
Review

Machine learning and radiology

Shijun Wang et al. Med Image Anal. 2012 Jul.

Abstract

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers.

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Figures

Fig. 1
Fig. 1
Connections between different areas of machine learning.
Fig. 2
Fig. 2
Best projection direction (purple arrow) found by LDA. Two different classes of data with “Gaussian-like” distributions are shown in different markers and ellipses. 1-D distributions of the two-classes after projection are also shown along the line perpendicular to the projection direction.
Fig. 3
Fig. 3
Illustration of margin learned by SVM. Black line is the best hyperplane which can separate the two classes of data with maximum margin. Support vectors are shown in circles.
Fig. 4
Fig. 4
Modeling of bone fractures using a Bayesian network in which the bone fracture variable is caused by the states of the weather (e.g., snowing) and car accidents on the road. Each table in the figure shows the probabilities of the corresponding variables given states of father nodes (indentified by arrows). Snow is an independent variable and we show its a priori probabilities in the adjacent table.
Fig. 5
Fig. 5
A hierarchical blob representation of a brain image. Right figure shows corresponding graph constructed from the blob image. Reproduced with permission from Ref. (Lehmann et al., 2004).
Fig. 6
Fig. 6
Pulmonary embolism (shown in yellow circle) in the artery of a 52-year old male patient.
Fig. 7
Fig. 7
Form of the model for predicting fMRI activation for arbitrary noun stimuli. fMRI activation is predicted in a two-step process. The first step encodes the meaning of the input stimulus word in terms of intermediate semantic features whose values are extracted from a large corpus of text exhibiting typical word use. The second step predicts the fMRI image as a linear combination of the fMRI signatures associated with each of these intermediate semantic features. Reproduced with permission from Ref. (Mitchell et al., 2008)
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