基于Faster-RCNN的结核杆菌自动检测方法研究与应用

Research and Application of Faster-RCNN Based M. Tuberculosis Detection Method

染色处理可使结核杆菌在显微镜拍摄的医学图像中显现,医生通过检测图像中的结核杆菌辅助诊断结核病。近年来卷积神经网络(CNN)在目标检测上取得了突破性进展,但结核杆菌存在图像上尺度小,构造标注数据难,不适用迁移学习等问题,使得基于CNN的目标检测方法在结核杆菌检测方面尚存在一定的困难。为此,以Faster-RCNN目标检测算法为基础,研究在医学图像上的结核杆菌检测问题。针对结核杆菌尺度小,提出重叠子图划分策略;针对标注数据构造难,提出分块、迭代标注策略。实践证明,该方法有较高的准确度以及可接受的速度,已构建了13 261个结核杆菌的训练数据,应用于合作单位的医疗检测产品,能满足实际应用需求。

Through sputum-smear staining,mycobacterium tuberculosis can be shown on microscope image,which makes it possible to detect M.tuberculosis on the image for facilitating tuberculosis diagnosis.On the microscope image,M.tuberculosis is characterized with diverse color saturation,various shape,and undistinguishable appearance confused with background,which make it a great challenge for traditional object detection methods.As convolutional neural networks(CNN)has achieved great success in object detection recently,we study CNN-based method,for instance,Faster-RCNN for M.tuberculosis detection.Nevertheless,there are still some problems with CNN-based tuberculosis detection:a)Size of M.tuberculosis on image is too small,b)Constructing enough accurate labeled data is difficult,and c)Transfer learning does not work for tuberculosis detection.All of those make it hard to apply CNN-based method to M.tuberculosis detection directly.To overcome these problems,we adopt two strategies.We present overlapping sub-image partition strategy for the small-size problem caused by anchor structure which is component of prevalent CNN-based object-detection method.The partition strategy overlappingly partition raw image into sub-images as per a formula presented by us.After partitioning,the proportion of M.tuberculosis on input image of model have been increased,that improving detecting accurate but reducing detecting speed.According to practice,we deem it acceptable.By cooperating with the co-author,13 261 labeled data of M.tuberculosis have been constructed.Through a series of experiments,it has proved that our method is effective not only in improving detecting accurate and generalization of the model,but also in reducing necessary labeled data.The methods have been integrated into medical inspection products and confirmed to satisfy practical application requirements.