3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding.
3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding.
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DOI：
10.3389/fbinf.2023.1275402
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Chang, Young Hwan
中科院分区：
文献类型：
--
作者：
Burlingame, Erik;Ternes, Luke;Lin, Jia-Ren;Chen, Yu-An;Kim, Eun Na;Gray, Joe W;Chang, Young Hwan
简介：基于组织的采样与诊断是指从一定的有限空间中提取信息并确定其对某一对象的诊断意义。病理学家处理与肿瘤异质性相关的问题，因为分析单个样本并不一定能获得癌症的代表性描述，组织活检通常只显示肿瘤的一小部分。许多多重组织成像平台（MTIs）假设包含二维（2D）组织切片的小核心样品的组织微阵列（TMA）是块体肿瘤的良好近似，尽管肿瘤不是2D的。然而，新兴的全载玻片成像（WSI）或3D肿瘤图谱，使用MTI如循环免疫荧光（CyCIF）强烈挑战这一假设。尽管通过测量WSI或3D中的肿瘤微环境收集了额外的见解，但使用CyCIF处理数十或数百个组织切片可能非常昂贵和耗时。即使当资源不受限制时，用于下游分析的组织中的感兴趣区域（ROI）选择的标准在很大程度上仍然是定性的和主观的，因为分层采样需要对象的知识并评估它们的特征。尽管TMA无法充分近似整个组织特征，但存在可以最好地代表整个载玻片图像中的肿瘤的组织的理论子采样。 研究方法：为了解决这些挑战，我们提出了深度学习方法来从两个方面学习多模态图像翻译任务：1）重建3D CyCIF表示的生成建模方法和2）共嵌入CyCIF图像和苏木素和伊红（H&E）部分来学习多模态映射通过跨域翻译以获得最小代表性投资回报率选择。 结果和讨论：我们证明了生成建模能够在训练时给定一小部分成像数据的结直肠癌标本的3D虚拟CyCIF重建。通过共同嵌入组织学和MTI特征，我们提出了一个简单的凸优化的目标ROI选择。我们展示了ROI选择的潜在应用及其在细胞异质性方面的性能效率。
Introduction: Tissue-based sampling and diagnosis are defined as the extraction of information from certain limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually only presents a small fraction of the tumor. Many multiplex tissue imaging platforms (MTIs) make the assumption that tissue microarrays (TMAs) containing small core samples of 2-dimensional (2D) tissue sections are a good approximation of bulk tumors although tumors are not 2D. However, emerging whole slide imaging (WSI) or 3D tumor atlases that use MTIs like cyclic immunofluorescence (CyCIF) strongly challenge this assumption. In spite of the additional insight gathered by measuring the tumor microenvironment in WSI or 3D, it can be prohibitively expensive and time-consuming to process tens or hundreds of tissue sections with CyCIF. Even when resources are not limited, the criteria for region of interest (ROI) selection in tissues for downstream analysis remain largely qualitative and subjective as stratified sampling requires the knowledge of objects and evaluates their features. Despite the fact TMAs fail to adequately approximate whole tissue features, a theoretical subsampling of tissue exists that can best represent the tumor in the whole slide image. Methods: To address these challenges, we propose deep learning approaches to learn multi-modal image translation tasks from two aspects: 1) generative modeling approach to reconstruct 3D CyCIF representation and 2) co-embedding CyCIF image and Hematoxylin and Eosin (H&E) section to learn multi-modal mappings by a cross-domain translation for minimum representative ROI selection. Results and discussion: We demonstrate that generative modeling enables a 3D virtual CyCIF reconstruction of a colorectal cancer specimen given a small subset of the imaging data at training time. By co-embedding histology and MTI features, we propose a simple convex optimization for objective ROI selection. We demonstrate the potential application of ROI selection and the efficiency of its performance with respect to cellular heterogeneity.
影响因子：
4.6
作者：
Ternes L;Huang G;Lanciault C;Thibault G;Riggers R;Gray JW;Muschler J;Chang YH
通讯作者：
Chang YH