Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach. (May 2021) - Koh, Joel En Wei; De Michele, Simona; Sudarshan, Vidya K; Jahmunah, V; Ciaccio, Edward J.; Ooi, Chui Ping; Gururajan, Raj; Gururajan, Rashmi; Oh, Shu Lih; Lewis, Suzanne K.; Green, Peter H.; Bhagat, Govind; et al.
Highlights: First machine learning technique for automated detection and classification of celiac disease using biopsy images. Steerable Pyramid Transform and nonlinear features are employed. Obtained classification accuracies of 88.89% and 82.92% using H&E and RGB stained biopsy images respectively. Also obtained an accuracy of 72% for the classification of multi-class biopsy images. Developed system can assist pathologists in accelerating the diagnostic process without bias, resulting in greater accuracy and earlier access to treatment. Abstract: Background and objectives: Celiac disease is an autoimmune disease occurring in about 1 in 100 people worldwide. Early diagnosis and efficient treatment are crucial in mitigating the complications that are associated with untreated celiac disease, such as intestinal lymphoma and malignancy, and the subsequent high morbidity. The current diagnostic methods using small intestinal biopsy histopathology, endoscopy, and video capsule endoscopy (VCE) involve manual interpretation of photomicrographs or images, which can be time-consuming and difficult, with inter-observer variability. In this paper, a machine learning technique was developed for the automation of biopsy image analysis to detect and classify villous atrophy based on modified Marsh scores. This is one of the first studies to employ conventional machine learning to automate the use of biopsy images for celiac disease detection and classification. Methods: The Steerable Pyramid Transform (SPT) method was used to obtain sub bands from which various types of entropy and nonlinear features were computed. All extracted features were automatically classified into two-class and multi-class, using six classifiers. Results: An accuracy of 88.89%, was achieved for the classification of two-class villous abnormalities based on analysis of Hematoxylin and Eosin (H&E) stained biopsy images. Similarly, an accuracy of 82.92% was achieved for the two-class classification of red-green-blue (RGB) biopsy images. Also, an accuracy of 72% was achieved in the classification of multi-class biopsy images. Conclusion: The results obtained are promising, and demonstrate the possibility of automating biopsy image interpretation using machine learning. This can assist pathologists in accelerating the diagnostic process without bias, resulting in greater accuracy, and ultimately, earlier access to treatment.