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International Journal of Computer Assisted Radiology and Surgery

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11-03-2017 | Original Article

A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists

Authors: Masami Kawagishi, Bin Chen, Daisuke Furukawa, Hiroyuki Sekiguchi, Koji Sakai, Takeshi Kubo, Masahiro Yakami, Koji Fujimoto, Ryo Sakamoto, Yutaka Emoto, Gakuto Aoyama, Yoshio Iizuka, Keita Nakagomi, Hiroyuki Yamamoto, Kaori Togashi

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 5/2017

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Abstract

Purpose

In our previous study, we developed a computer-aided diagnosis (CADx) system using imaging findings annotated by radiologists. The system, however, requires radiologists to input many imaging findings. In order to reduce such an interaction of radiologists, we further developed a CADx system using derived imaging findings based on calculated image features, in which the system only requires few user operations. The purpose of this study is to check whether calculated image features (CFT) or derived imaging findings (DFD) can represent information in imaging findings annotated by radiologists (AFD).

Methods

We calculate 2282 image features and derive 39 imaging findings by using information on a nodule position and its type (solid or ground-glass). These image features are categorized into shape features, texture features and imaging findings-specific features. Each imaging finding is derived based on each corresponding classifier using random forest. To check whether CFT or DFD can represent information in AFD, under an assumption that the accuracies of classifiers are the same if information included in input is the same, we constructed classifiers by using various types of information (CTT, DFD and AFD) and compared accuracies on an inferred diagnosis of a nodule. We employ SVM with RBF kernel as classifier to infer a diagnosis name.

Results

Accuracies of classifiers using DFD, CFT, AFD and CFT \(+\) AFD were 0.613, 0.577, 0.773 and 0.790, respectively. Concordance rates between DFD and AFD of shape findings, texture findings and surrounding findings were 0.644, 0.871 and 0.768, respectively.

Conclusions

The results suggest that CFT and AFD are similar information and CFT represent only a portion of AFD. Particularly, CFT did not contain shape information in AFD. In order to decrease an interaction of radiologists, a development of a method which overcomes these problems is necessary.

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Title: A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists
Authors: Masami Kawagishi
Bin Chen
Daisuke Furukawa
Hiroyuki Sekiguchi
Koji Sakai
Takeshi Kubo
Masahiro Yakami
Koji Fujimoto
Ryo Sakamoto
Yutaka Emoto
Gakuto Aoyama
Yoshio Iizuka
Keita Nakagomi
Hiroyuki Yamamoto
Kaori Togashi
Publication date: 11-03-2017
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 5/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1554-0

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Abstract

Purpose

Methods

Results

Conclusions

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