Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy

Abstract

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.

Hypothesis: Caco‐2 cell rotational 3D mechanogenomic turing patterns have clinical implications to colon crypts

“…cancer) actomyosin meshwork pattern(s). 3,28,29 Human colon epithelium demonstrates various patterns during colonoscopy that correlate with different physiological and pathological conditions, which can be recognized using recently developed artificial intelligence algorithms. 16,29 2D/3D patterns consistent with Turing's hypothesis appear to be present in our clinical data set (Figure 2A).…”

“…Therefore, it is feasible to combine these techniques to assess tight junction geometric patterns potentially useful to monitor the transformation of normal to abnormal (e.g. cancer) actomyosin meshwork pattern(s) . Human colon epithelium demonstrates various patterns during colonoscopy that correlate with different physiological and pathological conditions, which can be recognized using recently developed artificial intelligence algorithms .…”

An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

“…However, the strength of this proposed system is that it utilizes more than 100 standard videos from different sources including its own dataset. Most of the data have been collected from Department of Electronics, University of Alcala (http://www.depeca.uah.es/colonoscopy_dataset/) [16]. Another important source of data set is Endoscopic Vision Challenge (https://polyp.grand-challenge.org/databases/) [22].…”

Rectal cancer: Toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network

“…In case of the rectal cancer diagnosis, most studies have been mainly based on the hand‐crafted features such as features from vessels structures and from local patches . As above mentioned, these methods have limitation that they require researcher's knowledge such as the selection of the region‐of‐interest (ROI) for rectal tumor.…”

“…In case of the rectal cancer diagnosis, most studies have been mainly based on the hand-crafted features such as features from vessels structures and from local patches. [17][18][19][20] As above mentioned, these methods have limitation that they require researcher's knowledge such as the selection of the region-of-interest (ROI) for rectal tumor. Recently, some studies have reported successful results for applying deep learning technology to colorectal and rectal cancer diagnosis.…”