Ray Soumyajit, Lee Cheng-Yu, Park Hyeon-Cheol, Nauen David W, Bettegowda Chetan, Li Xingde, Chellappa Rama
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
Biomed Opt Express. 2025 Jul 21;16(8):3283-3294. doi: 10.1364/BOE.563694. eCollection 2025 Aug 1.
This study presents an approach to brain cancer detection based on optical coherence tomography (OCT) images and advanced machine learning techniques. The research addresses the critical need for accurate, real-time differentiation between cancerous and noncancerous brain tissue during neurosurgical procedures. The proposed method combines a pre-trained large vision transformer (ViT) model, specifically DINOv2, with a convolutional neural network (CNN) operating on the grey level co-occurrence matrix (GLCM) texture features. This dual-path architecture leverages both the global contextual feature extraction capabilities of transformers and the local texture analysis strengths of GLCM + CNNs. To mitigate patient-specific bias from the limited cohort, we incorporate an adversarial discriminator network that attempts to identify individual patients from feature representations, creating a competing objective that forces the model to learn generalizable cancer-indicative features rather than patient-specific characteristics. We also explore an alternative state space model approach using MambaVision blocks, which achieves comparable performance. The dataset comprised OCT images from 11 patients, with 5,831 B-frame slices from 7 patients used for training and validation, and 1,610 slices from 4 patients used for testing. The model achieved high accuracy in distinguishing cancerous from noncancerous tissue, with over 99% accuracy on the training dataset, 98.8% on the validation dataset and 98.6% accuracy on the test dataset. This approach demonstrates significant potential for achieving and improving intraoperative decision-making in brain cancer surgeries, offering real-time, high-accuracy tissue classification and surgical guidance.
本研究提出了一种基于光学相干断层扫描（OCT）图像和先进机器学习技术的脑癌检测方法。该研究满足了神经外科手术期间对癌性和非癌性脑组织进行准确、实时区分的迫切需求。所提出的方法将预训练的大型视觉Transformer（ViT）模型，特别是DINOv2，与基于灰度共生矩阵（GLCM）纹理特征运行的卷积神经网络（CNN）相结合。这种双路径架构利用了Transformer的全局上下文特征提取能力和GLCM + CNN的局部纹理分析优势。为了减轻有限队列中患者特异性偏差的影响，我们引入了一个对抗性判别器网络，该网络试图从特征表示中识别个体患者，创建一个竞争目标，迫使模型学习可推广的癌症指示特征而不是患者特异性特征。我们还探索了一种使用MambaVision块的替代状态空间模型方法，该方法取得了可比的性能。数据集包括11名患者的OCT图像，其中来自7名患者的5831个B帧切片用于训练和验证，来自4名患者的1610个切片用于测试。该模型在区分癌性和非癌性组织方面取得了高精度，在训练数据集上的准确率超过99%，在验证数据集上为98.8%，在测试数据集上为98.6%。这种方法在实现和改善脑癌手术中的术中决策方面显示出巨大潜力，提供实时、高精度的组织分类和手术指导。
