Introduction
Collagenous colitis (CC) is a form of microscopic colitis (MC), the others being lymphocytic colitis (LC), incomplete CC and incomplete LC (1). It is an inflammatory disease of the large bowel that causes chronic watery diarrhoea, abdominal pain, faecal incontinence, nightly defecation, and weight loss, resulting in significantly impaired quality of life (2). Incidence of MC has increased significantly during the past decades in some countries and the main reason for this increase is thought to be an enhanced disease recognition (3). The diagnosis of MC is challenging as it can only be diagnosed upon histological examination of colonic biopsies taken from normal or near normal appearing mucosa (1). Histological interpretation of biopsies involves subjective evaluation leading to inter-rater variability discrepancies in diagnosis and treatment plan. Deep learning-based assistance system can objectivise diagnostic key feature selection leading to minimisation of inter-rater variability and improvement of diagnostic accuracy.
Aims & Methods
The aim of this pilot study was to develop an algorithm for robust segmentation of light microscopy images of histological specimen slides (tissue slides) emphasizing key diagnostic features of CC.
Histological specimens were fixed with formalin, embedded in paraffin, cut into 3 µm sections, and stained with haematoxylin and eosin (H&E) for histological examination. Images were taken using OLYMPUS IX71 light microscope (x20 magnification) equipped with Q IMAGING EXI aqua camera at (1392 x 1040 pix.) resolution and 24-bit pixel encoding. Registered image pixel values were normalized in RGB colour space by histogram alignment using empty tissue-less areas using an algorithm described previously (4). Superpixel technique using Simple Linear Iterative Clustering algorithm (5) was used for initial segmentation of the images. All 1000 superpixels defined in each image were annotated by the expert, indicating ones containing thickened subepithelial collagen layer as the sought class among the others containing the rest of the tissue indicated as normal class. The feed – forward neural network (implemented in MatLab environment) was trained to classify superpixels using their normalized histogram bin-counts of the pixel values as a feature. Due to significant inequality in the data set, where the number of sought class superpixels was significantly smaller than the number of the remaining ones, the training set was formed by equalising it to the lowest class case’s value (18761). Only randomly selected cases of the bigger class were included in the training set.
Results
The proposed algorithm was tested on images from 5 treatment-naive patients with newly diagnosed CC. Patients' histological specimens were rated by an expert pathologist and 50 images were chosen (with changes characteristic of CC and without). The classification training process with extracted data set took 0.13 seconds on personal computer with an AMD Ryzen Threadripper 3970X 32-Core, 3.70 GHz processor and 128GB of RAM memory, epochs for training was set to 30 (after tests the model was not improving after 30 epochs) and data shuffling was set to every-epoch. The algorithm showed 0.807 accuracy, 0.801 sensitivity and 0.813 specificity.
Conclusion
The shown ability of deep learning algorithm to classify histology image segments can assist the diagnostic process of CC emphasizing thickened subepithelial collagen layer – the essential diagnostic feature.
References
1. Miehlke S, Guagnozzi D, Zabana Y, Tontini GE, Fiehn AMK, Wildt S, et al. European guidelines on microscopic colitis: United European Gastroenterology (UEG) and European Microscopic Colitis Group (EMCG) statements and recommendations. United European Gastroenterol J. 2020;
2. Verhaegh BPM, Münch A, Guagnozzi D, Wildt S, Cebula W, Diac AR, et al. Course of Disease in Patients with Microscopic Colitis: A European Prospective Incident Cohort Study. J Crohns Colitis. 2021;15(7):1174–83.
3. Weimers P, Ankersen DV, Lophaven S, Bonderup OK, Münch A, Løkkegaard ECL, et al. Incidence and Prevalence of Microscopic Colitis between 2001 and 2016: A Danish Nationwide Cohort Study. J Crohns Colitis. 2021;14(12):1717–23.
4. Petrolis R., Čižas P., Borutaitė V., Kriščiukaitis A. Method of fluorescence imaging for evaluation of membrane potential in cultured neurons using transmembrane voltage sensitive dye. Biomedical engineering 2011: Proceedings of International Conference. 14:16-19, 2011.
5. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S. SLIC Superpixels Compared to State-of-the-art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 34, Issue 11, pp. 2274-2282, May 2012