Soft tissue sarcoma (STS) is a locally aggressive and infiltrative tumour in dogs. Surgical resection is the treatment of choice for local tumour control. Currently, post-operative pathology is performed for surgical margin assessment. Spectral-domain optical coherence tomography (OCT) has recently been evaluated for its value for surgical margin assessment in some tumour types in dogs. The purpose of this study was to develop an automatic diagnosis system that can assist clinicians in real-time for OCT image interpretation of tissues at surgical margins. We utilized a ResNet-50 network to classify healthy and cancerous tissues. A patch-based approach was adopted to achieve accurate classification with limited training data (80 cancer images, 80 normal images) and the validation set (20 cancer images, 20 normal images). The proposed method achieved an average accuracy of 97.1% with an excellent sensitivity of 94.3% on the validation set; the quadratic weighted κ was 0.94 for the STS diagnosis. In an independent test data set of 20 OCT images (10 cancer images, 10 normal images), the proposed method correctly differentiated all the STS images. Furthermore, we proposed a diagnostic curve, which could be evaluated in real-time to assist clinicians in detecting the specific location of a lesion. In short, the proposed method is accurate, operates in real-time and is non-invasive, which could be helpful for future surgical guidance.

中文翻译：

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深度学习增强光学相干断层扫描对犬软组织肉瘤的术中评估

软组织肉瘤 (STS) 是犬的一种局部侵袭性和浸润性肿瘤。手术切除是局部肿瘤控制的首选治疗方法。目前，进行术后病理学以评估手术切缘。最近评估了光谱域光学相干断层扫描 (OCT) 对狗的某些肿瘤类型的手术切缘评估的价值。本研究的目的是开发一种自动诊断系统，可以帮助临床医生实时对手术边缘组织的 OCT 图像进行解释。我们利用 ResNet-50 网络对健康组织和癌组织进行分类。采用基于补丁的方法，以有限的训练数据（80 个癌症图像，80 个正常图像）和验证集（20 个癌症图像，20 个正常图像）实现准确分类。所提出的方法在验证集上实现了 97.1% 的平均准确率和 94.3% 的出色灵敏度；STS 诊断的二次加权 κ 为 0.94。在包含 20 张 OCT 图像（10 张癌症图像，10 张正常图像）的独立测试数据集中，所提出的方法正确区分了所有 STS 图像。此外，我们提出了一个诊断曲线，可以实时评估，以帮助临床医生检测病变的具体位置。总之，所提出的方法准确、实时、无创，有助于未来的手术指导。所提出的方法正确区分了所有 STS 图像。此外，我们提出了一个诊断曲线，可以实时评估，以帮助临床医生检测病变的具体位置。总之，所提出的方法准确、实时、无创，有助于未来的手术指导。所提出的方法正确区分了所有 STS 图像。此外，我们提出了一个诊断曲线，可以实时评估，以帮助临床医生检测病变的具体位置。总之，所提出的方法准确、实时、无创，有助于未来的手术指导。