Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy
Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy
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DOI：
10.1109/tmi.2016.2547947
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发表时间：
2016-09-01
影响因子：
10.6
通讯作者：
Bartoli, Adrien
中科院分区：
文献类型：
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作者：
Mesejo, Pablo;Pizarro, Daniel;Bartoli, Adrien
我们已经开发了一种技术来研究计算机在从常规（白光和窄带）结肠镜视频中诊断胃肠道病变方面与两个临床知识水平（专家和初学者）相比有多好。我们的技术包括一种新的组织分类方法，可以节省临床医生的时间，避免了使用靛蓝胭脂红进行染色的色素内镜检查。我们的技术还可以区分多个息肉患者个别病变的严重程度，以便胃肠病学家可以直接关注那些需要息肉切除术的患者。从技术上讲，我们设计并开发了一个结合机器学习和计算机视觉算法的框架，该框架可以对增殖性病变、锯齿状腺瘤和腺瘤进行虚拟活检。由于其混合/杂交性质，锯齿状腺瘤很难分类，最近的研究表明，它们可以通过交替的锯齿状途径导致结直肠癌。我们的方法是避免对疑似增生组织进行系统活检的第一步。我们还提出了一个数据库的结肠镜视频显示胃肠道病变与地面真相收集从专家图像检查和组织学。我们不仅将我们的系统与专家预测进行了比较，还研究了3D形状特征的使用是否提高了分类精度，并将我们的技术与三种竞争对手的方法进行了比较。
We have developed a technique to study how good computers can be at diagnosing gastrointestinal lesions from regular (white light and narrow banded) colonoscopic videos compared to two levels of clinical knowledge (expert and beginner). Our technique includes a novel tissue classification approach which may save clinician's time by avoiding chromoendoscopy, a time-consuming staining procedure using indigo carmine. Our technique also discriminates the severity of individual lesions in patients with many polyps, so that the gastroenterologist can directly focus on those requiring polypectomy. Technically, we have designed and developed a framework combining machine learning and computer vision algorithms, which performs a virtual biopsy of hyperplastic lesions, serrated adenomas and adenomas. Serrated adenomas are very difficult to classify due to their mixed/hybrid nature and recent studies indicate that they can lead to colorectal cancer through the alternate serrated pathway. Our approach is the first step to avoid systematic biopsy for suspected hyperplastic tissues. We also propose a database of colonoscopic videos showing gastrointestinal lesions with ground truth collected from both expert image inspection and histology. We not only compare our system with the expert predictions, but we also study if the use of 3D shape features improves classification accuracy, and compare our technique's performance with three competitor methods.