ESAT-PSI, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium; Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul, South Korea.
ESAT-PSI, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
Comput Med Imaging Graph. 2018 Nov;69:21-32. doi: 10.1016/j.compmedimag.2018.06.002. Epub 2018 Jul 6.
Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. This work explores automated margin assessment on a sample of patient data collected at the Pathology Department, Severance Hospital (Seoul, South Korea). Some methods based on the spatial statistics of the images have been developed, but the obtained results are still far from human performance. In this work, we investigate the possibility to use deep neural networks (DNNs) for real time margin assessment, demonstrating performance significantly better than the reported literature and close to the level of a human expert. Since the goal is to detect the presence of cancer, a patch-based classification method is proposed, as it is sufficient for detection, and requires training data that is easier and cheaper to collect than for other approaches such as segmentation. For that purpose, we train a DNN architecture that was proved to be efficient for small images on patches extracted from images containing only cancer or only normal tissue as determined by pathologists in a university hospital. As the number of available images in all such studies is by necessity small relative to other deep network applications such as ImageNet, a good regularization method is needed. In this work, we propose to use a recently introduced function norm regularization that attempts to directly control the function complexity, in contrast to classical approaches such as weight decay and DropOut. As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function norm regularization yielded higher and more robust results than competing regularization methods. We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12% (the lowest reported in the literature) to 5% - a 58% reduction. The method is computationally feasible for intraoperative application (less than 2 s per image) at the only cost of a longer offline training time.
在乳腺肿瘤切除术期间评估手术切缘可以避免进行额外的手术。光学相干断层扫描（OCT）已被证明是一种有效的成像技术，可用于此目的。然而，为了避免手术过程中给外科医生带来过重的负担，需要在切除组织的表面自动检测癌症。这项工作探索了在病理学系收集的患者数据样本上进行自动切缘评估。已经开发了一些基于图像空间统计的方法，但所获得的结果仍远不及人类表现。在这项工作中，我们研究了使用深度神经网络（DNN）进行实时切缘评估的可能性，证明了性能明显优于文献报道的结果，并且接近人类专家的水平。由于目标是检测癌症的存在，因此提出了基于补丁的分类方法，因为它足以进行检测，并且所需的训练数据比其他方法（例如分割）更容易且更便宜。为此，我们训练了一种 DNN 架构，该架构在从仅包含癌症或仅由大学医院病理学家确定为正常组织的图像中提取的补丁上进行训练，证明对小图像非常有效。由于与其他深度网络应用（例如 ImageNet）相比，所有此类研究中的可用图像数量必然较小，因此需要一种良好的正则化方法。在这项工作中，我们建议使用最近引入的函数范数正则化方法，该方法试图直接控制函数的复杂性，与经典的方法（例如权重衰减和 DropOut）形成对比。由于以前结果的代码和数据均未公开，因此进行了保守比较，将获得的结果与文献中的报告结果进行了比较。此外，我们的方法还应用于在几种数据配置下收集的本地数据。报告的结果是不同试验的平均值。实验结果表明，在灵敏度、特异性、F1 得分、G-均值和马修斯相关系数方面，与其他技术相比，使用 DNN 可以产生更好的结果。函数范数正则化方法产生的结果优于竞争正则化方法。我们已经展示了一个系统，该系统在（部分）自动评估人乳腺组织的切缘方面显示出很高的前景，等错误率（EER）从大约 12%（文献中报告的最低值）降低到 5%至 58%。该方法在计算上可用于术中应用（每张图像少于 2 秒），唯一的代价是更长的离线训练时间。
