Abstract：
OBJECTIVE: It is very difficult for a radiologist to correctly detect small lesions and lesions hidden on dense breast tissue on a mammogram. Therefore, recently, computer-aided detection (CAD) systems have been widely used to assist radiologists in interpreting images. Thus, in this study, we aimed to segment mass on the mammogram with high accuracy by using focus images obtained from an eye-tracking device.
METHODS: We obtained focus images for two mammography expert radiologists and 19 mammography technologists on 8 abnormal and 8 normal mammograms published by the DDSM. Next, the auto-encoder, Pix2Pix, and UNIT learned the relationship between the actual mammogram and the focus image, and generated the focus image for the unknown mammogram. Finally, we segmented regions of mass on mammogram using the U-Net for each focus image generated by the auto-encoder, Pix2Pix, and UNIT.
RESULTS: The dice coefficient in the UNIT was 0.64±0.14. The dice coefficient in the UNIT was higher than that in the auto-encoder and Pix2Pix, and there was a statistically significant difference (p<0.05). The dice coefficient of the proposed method, which combines the focus images generated by the UNIT and the original mammogram, was 0.66±0.15, which is equivalent to the method using the original mammogram.
CONCLUSIONS: In the future, it will be necessary to increase the number of cases and further improve the segmentation.
METHODS: We obtained focus images for two mammography expert radiologists and 19 mammography technologists on 8 abnormal and 8 normal mammograms published by the DDSM. Next, the auto-encoder, Pix2Pix, and UNIT learned the relationship between the actual mammogram and the focus image, and generated the focus image for the unknown mammogram. Finally, we segmented regions of mass on mammogram using the U-Net for each focus image generated by the auto-encoder, Pix2Pix, and UNIT.
RESULTS: The dice coefficient in the UNIT was 0.64±0.14. The dice coefficient in the UNIT was higher than that in the auto-encoder and Pix2Pix, and there was a statistically significant difference (p<0.05). The dice coefficient of the proposed method, which combines the focus images generated by the UNIT and the original mammogram, was 0.66±0.15, which is equivalent to the method using the original mammogram.
CONCLUSIONS: In the future, it will be necessary to increase the number of cases and further improve the segmentation.
摘要：
目的：放射科医生很难在乳房X线照片上正确检测到微小病变和隐藏在致密乳腺组织上的病变。因此,最近,计算机辅助检测（CAD）系统已广泛用于辅助放射科医生解释图像。因此，在这项研究中，我们的目标是通过使用从眼动追踪设备获得的聚焦图像，在乳房X线照片上高精度地分割肿块。
方法：我们在DDSM发布的8例异常和8例正常乳房X线照片中获得了两名乳房X线摄影专家和19名乳房X线摄影技术人员的焦点图像。接下来,自动编码器，Pix2Pix,UNIT了解了实际乳房X线照片和焦点图像之间的关系，并生成未知乳房X线照片的焦点图像。最后,我们使用U-Net为自动编码器生成的每个焦点图像在乳房X线照片上分割质量区域，Pix2Pix,和单位。
结果：单元中的骰子系数为0.64±0.14。单元中的骰子系数高于自动编码器和Pix2Pix中的骰子系数，差异有统计学意义(p<0.05)。提出的方法的骰子系数，它结合了UNIT生成的焦点图像和原始乳房X线照片，为0.66±0.15，相当于使用原始乳房X线照片的方法。
结论：在未来，有必要增加病例数量并进一步改善分割。
方法：我们在DDSM发布的8例异常和8例正常乳房X线照片中获得了两名乳房X线摄影专家和19名乳房X线摄影技术人员的焦点图像。接下来,自动编码器，Pix2Pix,UNIT了解了实际乳房X线照片和焦点图像之间的关系，并生成未知乳房X线照片的焦点图像。最后,我们使用U-Net为自动编码器生成的每个焦点图像在乳房X线照片上分割质量区域，Pix2Pix,和单位。
结果：单元中的骰子系数为0.64±0.14。单元中的骰子系数高于自动编码器和Pix2Pix中的骰子系数，差异有统计学意义(p<0.05)。提出的方法的骰子系数，它结合了UNIT生成的焦点图像和原始乳房X线照片，为0.66±0.15，相当于使用原始乳房X线照片的方法。
结论：在未来，有必要增加病例数量并进一步改善分割。
