Hanaoka Shouhei, Nomura Yukihiro, Hayashi Naoto, Sato Issei, Miki Soichiro, Yoshikawa Takeharu, Shibata Hisaichi, Nakao Takahiro, Takenaga Tomomi, Koyama Hiroaki, Cho Shinichi, Kanemaru Noriko, Fujimoto Kotaro, Sakamoto Naoya, Nishiyama Tomoya, Matsuzaki Hirotaka, Yamamichi Nobutake, Abe Osamu
Department of Radiology and Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan.
Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan.
PLoS One. 2024 Dec 12;19(12):e0315646. doi: 10.1371/journal.pone.0315646. eCollection 2024.
A general-purpose method of emphasizing abnormal lesions in chest radiographs, named EGGPALE (Extrapolative, Generative and General-Purpose Abnormal Lesion Emphasizer), is presented. The proposed EGGPALE method is composed of a flow-based generative model and L-infinity-distance-based extrapolation in a latent space. The flow-based model is trained using only normal chest radiographs, and an invertible mapping function from the image space to the latent space is determined. In the latent space, a given unseen image is extrapolated so that the image point moves away from the normal chest X-ray hyperplane. Finally, the moved point is mapped back to the image space and the corresponding emphasized image is created. The proposed method was evaluated by an image interpretation experiment with nine radiologists and 1,000 chest radiographs, of which positive suspected lung cancer cases and negative cases were validated by computed tomography examinations. The sensitivity of EGGPALE-processed images showed +0.0559 average improvement compared with that of the original images, with -0.0192 deterioration of average specificity. The area under the receiver operating characteristic curve of the ensemble of nine radiologists showed a statistically significant improvement. From these results, the feasibility of EGGPALE for enhancing abnormal lesions was validated. Our code is available at https://github.com/utrad-ical/Eggpale.
本文提出了一种用于强调胸部X光片中异常病变的通用方法，名为EGGPALE（外推、生成式和通用异常病变增强器）。所提出的EGGPALE方法由基于流的生成模型和潜在空间中基于L无穷范数距离的外推组成。基于流的模型仅使用正常胸部X光片进行训练，并确定从图像空间到潜在空间的可逆映射函数。在潜在空间中，对给定的未见图像进行外推，使图像点远离正常胸部X光超平面。最后，将移动后的点映射回图像空间，创建相应的增强图像。通过与九名放射科医生进行的图像解读实验以及1000张胸部X光片对所提出的方法进行了评估，其中阳性疑似肺癌病例和阴性病例通过计算机断层扫描检查进行了验证。与原始图像相比，EGGPALE处理后的图像的灵敏度平均提高了+0.0559，平均特异性下降了-0.0192。九名放射科医生的集合在接收者操作特征曲线上的面积显示出统计学上的显著改善。从这些结果来看，EGGPALE增强异常病变的可行性得到了验证。我们的代码可在https://github.com/utrad-ical/Eggpale获取。
