Projektakademie Medizintechnik: LearnBarrida - Image Analysis and Machine Learning for Barrett Esophagus: Identification of Dysplasia and Adenocarcinoma

项目学院医疗技术：LearnBarrida - Barrett 食管的图像分析和机器学习：异常增生和腺癌的识别

• 批准号: 332376560
• 负责人: Professor Dr. Christoph Palm
• 金额: --
• 依托单位: Fakultät Informatik und Mathematik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/332376560?language=en
• 关键词: Projektakademie; Medizintechnik; LearnBarrida; Image; Analysis

Barrett is a degeneration of mucosal tissue in the esophagus. It is known as precursor of esophageal adenocarcinoma (EAC). Only diagnosed in an early stage of high grade dysplasia (HGD) or carcinoma patients have a good prognosis. EAC is the cancer, which shows the largest increase in the western world. Unfortunately, it is very difficult to identify EAC or HGD during an endoscopic procedure.Therefore, within the project LearnBarrIDA methods of medical image computing and machine learning will be adapted and enhanced to enable automatic analysis of endoscopic color images and extract relevant features for diagnosis. The availability of a mature computer assisted diagnosis system would help physicians to reduce the number of wrong diagnoses in cases of EAC/HGD significantly followed by a better prognosis for the patients.

巴雷特是一种食管粘膜组织的变性。它被称为食管腺癌（EAC）的前体。只有在早期诊断的高度异型增生（HGD）或癌患者预后良好。EAC是癌症，在西方世界增长最快。不幸的是，在内窥镜检查过程中很难识别EAC或HGD。因此，在项目LearnBarrIDA中，医学图像计算和机器学习的方法将被调整和增强，以实现内窥镜彩色图像的自动分析，并提取相关特征用于诊断。一个成熟的计算机辅助诊断系统的可用性将有助于医生减少EAC/HGD病例中的错误诊断数量，从而为患者提供更好的预后。