Automated Classification of Blood Loss from Transurethral Resection of the Prostate Surgery Videos Using Deep Learning Technique
Automated Classification of Blood Loss from Transurethral Resection of the Prostate Surgery Videos Using Deep Learning Technique
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DOI：
10.3390/app10144908
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发表时间：
2020-07-01
影响因子：
2.7
通讯作者：
Tang, Chuan-Yi
中科院分区：
文献类型：
--
作者：
Chen, Jian-Wen;Lin, Wan-Ju;Tang, Chuan-Yi
经尿道前列腺切除术（TURP）是一种切除阻塞性前列腺组织的手术。总出血面积用于确定 TURP 手术的效果。传统的出血区域检测方法虽然可以提供准确的结果，但无法及时检测出血区域以进行手术诊断。此外，由于手术切割环产生的红光图案经常出现在图像上，因此很容易干扰经验丰富的医生判断出血区域。近年来，自动计算机辅助技术和人工智能深度学习在医学图像识别中得到广泛应用，可以有效地提取所需的特征，减轻医生的负担，提高诊断的准确性。在这项研究中，我们集成了两种最先进的深度学习技术，用于识别和提取 TURP 手术中切割环产生的红光区域。首先，ResNet-50 模型用于识别手术视频碎片帧中出现的红光图案。然后，使用所提出的 Res-Unet 模型来分割具有红光图案的区域并去除这些区域。最后利用色调、饱和度、明度色彩空间对非红光图案图像情况下失血量的四个级别进行分类。实验表明，所提出的Res-Unet模型在红光和非红光图像分类方面比其他分割算法具有更高的精度，并且能够提取TURP手术图像中的红光图案并有效去除它们。这里提出的方法能够获得失血量的级别分类，这有助于医生的诊断。
Transurethral resection of the prostate (TURP) is a surgical removal of obstructing prostate tissue. The total bleeding area is used to determine the performance of the TURP surgery. Although the traditional method for the detection of bleeding areas provides accurate results, it cannot detect them in time for surgery diagnosis. Moreover, it is easily disturbed to judge bleeding areas for experienced physicians because a red light pattern arising from the surgical cutting loop often appears on the images. Recently, the automatic computer-aided technique and artificial intelligence deep learning are broadly used in medical image recognition, which can effectively extract the desired features to reduce the burden of physicians and increase the accuracy of diagnosis. In this study, we integrated two state-of-the-art deep learning techniques for recognizing and extracting the red light areas arising from the cutting loop in the TURP surgery. First, the ResNet-50 model was used to recognize the red light pattern appearing in the chipped frames of the surgery videos. Then, the proposed Res-Unet model was used to segment the areas with the red light pattern and remove these areas. Finally, the hue, saturation, and value color space were used to classify the four levels of the blood loss under the circumstances of non-red light pattern images. The experiments have shown that the proposed Res-Unet model achieves higher accuracy than other segmentation algorithms in classifying the images with the red and non-red lights, and is able to extract the red light patterns and effectively remove them in the TURP surgery images. The proposed approaches presented here are capable of obtaining the level classifications of blood loss, which are helpful for physicians in diagnosis.