A deep convolutional neural network-based automatic detection of brain metastases with and without blood vessel suppression
A deep convolutional neural network-based automatic detection of brain metastases with and without blood vessel suppression
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DOI：
10.1007/s00330-021-08427-2
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发表时间：
2022-01-07
影响因子：
5.9
通讯作者：
Hiwatashi, Akio
中科院分区：
文献类型：
--
作者：
Kikuchi, Yoshitomo;Togao, Osamu;Hiwatashi, Akio
目的建立一种基于卷积神经网络（CNN）和体积各向同性同时交错亮血和黑血检查（VISIBLE）的脑转移瘤自动检测模型，并将其诊断性能与观察者检验进行比较。方法回顾性研究2016年3月至2019年7月期间临床疑似脑转移的患者行可见显像，建立模型。使用有血管抑制和没有血管抑制的图像来训练现有的CNN （DeepMedic）。通过敏感性和每例假阳性结果（FPs/case）评估诊断性能。我们将CNN模型的诊断性能与12位放射科医生的诊断性能进行了比较。结果随访临床诊断为脑转移的患者50例（男30例，女20例，年龄29 ~ 86岁，平均63.3±12.8岁，共165例转移）参加培训。我们的模型灵敏度为91.7%，高于观察者检验（平均值+/-标准差；88.7 +/- 3.7%）。我们模型的FPs/case数为1.5，大于观察者检验的结果（0.17 +/- 0.09）。结论与放射科医生相比，我们由VISIBLE和CNN建立的诊断脑转移的模型具有更高的敏感性。我们的模型的FPs/病例数大于放射科医师的观察者测试；然而，在之前的大多数深度学习研究中，这个数值都小于这个数值。
Objectives To develop an automated model to detect brain metastases using a convolutional neural network (CNN) and volume isotropic simultaneous interleaved bright-blood and black-blood examination (VISIBLE) and to compare its diagnostic performance with the observer test. Methods This retrospective study included patients with clinical suspicion of brain metastases imaged with VISIBLE from March 2016 to July 2019 to create a model. Images with and without blood vessel suppression were used for training an existing CNN (DeepMedic). Diagnostic performance was evaluated using sensitivity and false-positive results per case (FPs/case). We compared the diagnostic performance of the CNN model with that of the twelve radiologists. Results Fifty patients (30 males and 20 females; age range 29-86 years; mean 63.3 +/- 12.8 years; a total of 165 metastases) who were clinically diagnosed with brain metastasis on follow-up were used for the training. The sensitivity of our model was 91.7%, which was higher than that of the observer test (mean +/- standard deviation; 88.7 +/- 3.7%). The number of FPs/case in our model was 1.5, which was greater than that by the observer test (0.17 +/- 0.09). Conclusions Compared to radiologists, our model created by VISIBLE and CNN to diagnose brain metastases showed higher sensitivity. The number of FPs/case by our model was greater than that by the observer test of radiologists; however, it was less than that in most of the previous studies with deep learning.