QuBBD: Collaborative Research: Quantifying Morphologic Phenotypes in Prostate Cancer - Developing Topological Descriptors for Machine Learning Algorithms

QuBBD：合作研究：量化前列腺癌的形态表型 - 开发机器学习算法的拓扑描述符

基本信息

批准号：
1664848
负责人：
Carola Wenk
金额：
$ 47.93万
依托单位：
Tulane University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664848&HistoricalAwards=false
关键词：
QuBBD Collaborative Research Quantifying Morphologic

项目摘要

The long-term goal of this project is to develop quantitative methodology for detecting geometric and topological features in point clouds extracted from (histology) images. Of particular relevance, this project considers the setting of prostate cancer classification, which is based on a pathologist grading of histology slides using the Gleason grading system. These pathology slides are a source of biomedical big data that are increasingly available as archived material. Developing these quantitative methods will be a significant advance towards a (semi-)automated quantification of prostate cancer aggressiveness. This award supports an interdisciplinary team of investigators in computational mathematics, computer science, biomedical engineering, and pathology to develop mathematical and computational tools based on topological descriptors and machine learning in order to distinguish between different morphological types of prostate cancer.This research will develop quantitative topological descriptors (e.g., persistence diagrams and summaries) that describe natural histologic phenotypes in prostate cancer, in order to provide explanatory information to assist in providing improved diagnostics/prognostics and insight into the best course of treatment for the patient. This will be accomplished through developing graphical models via unsupervised machine learning that increase our understanding of prostate cancer subtypes. The long-term goal is to develop imaging biomarkers that better identify indolent from aggressive prostate cancer compared to existing, subjective, and variable human observer analyses (i.e., the Gleason score). This project takes steps towards a novel quantitative methodology for prostate cancer classification, as well as towards developing topological methods for statistically distinguishing different types of glandular architectures.

该项目的长期目标是开发定量方法，用于从（组织学）图像中提取的点云中检测几何和拓扑特征。该项目特别相关，考虑了前列腺癌分类的设置，该设置基于使用格里森分级系统的病理学家分级滑动的病理学家分级。这些病理幻灯片是生物医学大数据的来源，这些数据越来越多地作为存档材料可用。开发这些定量方法将是朝着（半）自动定量前列腺癌侵略性定量的重大进步。该奖项支持计算数学，计算机科学，生物医学工程和病理学领域的研究人员跨学科团队，以基于拓扑描述符和机器学习来开发数学和计算工具，以区分这些研究的形态学类型。提供解释性信息，以帮助提供改进的诊断/预后，并洞悉患者的最佳治疗方法。这将通过通过无监督的机器学习来开发图形模型来实现，从而增加我们对前列腺癌亚型的理解。长期目标是开发成像生物标志物，与现有，主观和可变的人类观察者分析（即格里森评分）相比，可以更好地识别侵略性前列腺癌的懒惰。该项目采取了针对前列腺癌分类的新型定量方法的步骤，以及开发拓扑方法以统计上区分不同类型的腺体体系结构。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Persistence Atlas for Critical Point Variability in Ensembles

DOI：
10.1109/tvcg.2018.2864432
发表时间：
2018-07
期刊：
IEEE Transactions on Visualization and Computer Graphics
影响因子：
5.2
作者：
Guillaume Favelier;Noura Faraj;B. Summa;Julien Tierny
通讯作者：
Guillaume Favelier;Noura Faraj;B. Summa;Julien Tierny

Flexible Live-Wire: Image Segmentation with Floating Anchors

灵活的火线：使用浮动锚点进行图像分割

DOI：
10.1111/cgf.13364
发表时间：
2018
期刊：
Computer Graphics Forum
影响因子：
2.5
作者：
Summa, B.;Faraj, N.;Licorish, C.;Pascucci, V.
通讯作者：
Pascucci, V.

Adaptive Compositing and Navigation of Variable Resolution Images

DOI：
10.1111/cgf.14178
发表时间：
2020-10
期刊：
Computer Graphics Forum
影响因子：
2.5
作者：
C. Licorish;Noura Faraj;B. Summa
通讯作者：
C. Licorish;Noura Faraj;B. Summa

Efficient and Flexible Hierarchical Data Layouts for a Unified Encoding of Scalar Field Precision and Resolution

DOI：
10.1109/tvcg.2020.3030381
发表时间：
2020-10
期刊：
IEEE Transactions on Visualization and Computer Graphics
影响因子：
5.2
作者：
D. Hoang;B. Summa;H. Bhatia;Peter Lindstrom;Pavol Klacansky;W. Usher;P. Bremer;Valerio Pascucci
通讯作者：
D. Hoang;B. Summa;H. Bhatia;Peter Lindstrom;Pavol Klacansky;W. Usher;P. Bremer;Valerio Pascucci

A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering