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首页> 外文会议>Conference on Optical Coherence tomography and coherence domain optical methods in biomedicine XXI >Extracting relevant information for cancer diagnosis from dynamic full field OCT through image processing and learning
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Extracting relevant information for cancer diagnosis from dynamic full field OCT through image processing and learning

机译：通过图像处理和学习提取动态全场OCT的癌症诊断相关信息

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For a large number of cancer surgeries, the lack of reliable intraoperative diagnosis leads to reoperations or bad outcomes for the patients. To deliver better diagnosis, we developed Dynamic Full Field OCT (D-FFOCT) as a complement to FFOCT. FFOCT already presents interesting results for cancer diagnosis e.g. Mohs surgery and reaching 96% accuracy on prostate cancer. D-FFOCT accesses the dynamic processes of metabolism and gives new tools to diagnose the state of a tissue at the cellular level to complement FFOCT contrast. We developed a processing framework that intends to maximize the information provided by the FFOCT technology as well as D-FFOCT and synthetize this as a meaningful image. We use different time processing to generate metrics (standard deviation of time signals, decorrelation times and more) and spatial processing to sort out structures and the corresponding imaging modality, which is the most appropriate. Sorting was achieved through quadratic discriminant analysis in a N-dimension parametric space corresponding to our metrics. Combining the best imaging modalities for each structure leads to a rich morphology image. This image displaying the morphology is then colored to represent the dynamic behavior of these structures (slow or fast) and to be quickly analyzed by doctors. Therefore, we achieved a micron resolved image, rich of both FFOCT ability of imaging fixed and highly backscattering structures as well as D-FFOCT ability of imaging low level scattering cellular level details. We believe that this morphological contrast close to histology and the dynamic behavior contrast will push forward the limits of intraoperative diagnosis further on.
机译：对于大量癌症手术，缺乏可靠的术中诊断导致患者的重新进展或不良结果。为了提供更好的诊断，我们开发了动态全场OCT（D-FFOCT）作为FFOCT的补充。 FFOCT已经提出了癌症诊断的有趣结果。莫赫斯手术并达到前列腺癌的96％的准确性。 D-FFOCT访问新陈代谢的动态过程，并提供新工具，以诊断细胞水平的组织状态以补充FFOCT对比度。我们开发了一个加工框架，打算最大化FFOCT技术提供的信息以及D-FFOCT并合成这是一个有意义的图像。我们使用不同的时间处理来生成度量（时间信号，去相关时间和更多）和空间处理的标准偏差，以分类结构和相应的成像模态，这是最合适的。通过与我们的指标对应的n维参数空间中的二次判别分析来实现排序。组合每个结构的最佳成像模态导致丰富的形态学图像。然后，显示形态的图像是有色的，以表示这些结构的动态行为（慢速或快速），并通过医生快速分析。因此，我们实现了微米分辨的图像，丰富了成像固定和高度反向散射结构的FFOCT能力以及成像低水平散射细胞水平细节的D-FFOCT能力。我们认为，这种形态对比与组织学和动态行为对比度相比将进一步推动术中诊断的限制。

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