Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (_0.0141) sensitivity and 0.8094 (_0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (_0.0197) sensitivity and 0.7865 (_0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.This work was partially supported by PICCOLO project. This project has received funding from the European Union’s Horizon2020 Research and Innovation Programme under grant agreement No. 732111. This research has also received funding from the Basque Government’s Industry Department under the ELKARTEK program’s project ONKOTOOLS under agreement KK-2020/00069 and the industrial doctorate program UC- DI14 of the University of Cantabria

PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets

Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for e_ective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four di_erent models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.This work was partially supported by PICCOLO project. This project has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement No 732111. Furthermore, this publication has also been partially supported by GR18199 from Consejería de Economía, Ciencia y Agenda Digital of Junta de Extremadura (co-funded by European Regional Development Fund–ERDF. “A way to make Europe”/ “Investing in your future”. This work has been performed by the ICTS “NANBIOSIS” at the Jesús Usón Minimally Invasive Surgery Centre

Analysis on the characterization of multiphoton microscopy images for malignant neoplastic colon lesion detection under deep learning methods

Colorectal cancer has a high incidence rate worldwide, with over 1.8 million new cases and 880,792 deaths in 2018. Fortunately, its early detection significantly increases the survival rate, reaching a cure rate of 90% when diagnosed at a localized stage. Colonoscopy is the gold standard technique for detection and removal of colorectal lesions with potential to evolve into cancer. When polyps are found in a patient, the current procedure is their complete removal. However, in this process, gastroenterologists cannot assure complete resection and clean margins which are given by the histopathology analysis of the removed tissue, which is performed at laboratory. Aims: In this paper, we demonstrate the capabilities of multiphoton microscopy (MPM) technology to provide imaging biomarkers that can be extracted by deep learning techniques to identify malignant neoplastic colon lesions and distinguish them from healthy, hyperplastic, or benign neoplastic tissue, without the need for histopathological staining. Materials and Methods: To this end, we present a novel MPM public dataset containing 14,712 images obtained from 42 patients and grouped into 2 classes. A convolutional neural network is trained on this dataset and a spatially coherent predictions scheme is applied for performance improvement. Results: We obtained a sensitivity of 0.8228 ± 0.1575 and a specificity of 0.9114 ± 0.0814 on detecting malignant neoplastic lesions. We also validated this approach to estimate the self-confidence of the network on its own predictions, obtaining a mean sensitivity of 0.8697 and a mean specificity of 0.9524 with the 18.67% of the images classified as uncertain. Conclusions: This work lays the foundations for performing in vivo optical colon biopsies by combining this novel imaging technology together with deep learning algorithms, hence avoiding unnecessary polyp resection and allowing in situ diagnosis assessment.The authors would like to thank Roberto Bilbao, director of the Basque Biobank, Ainara Egia Bizkarralegorra and biobank technicians from Basurto University Hospital (Spain) and pathologist Prof. Rob Goldin from Imperial College London (UK)

The C-terminal transmembrane domain of human phospholipid scramblase 1 is essential for the protein flip-flop activity and Ca2+-binding

Human phospholipid scramblase 1 (SCR) is a 318 amino acid protein that was originally described as catalyzing phospholipid transbilayer (flip-flop) motion in plasma membranes in a Ca2+-dependent, ATP-independent way. Further studies have suggested an intranuclear role for this protein in addition. A putative transmembrane domain located at the C terminus (aa 291\u2013309) has been related to the flip-flop catalysis. In order to clarify the role of the C-terminal region of SCR, a mutant was produced (SCR\u394) in which the last 28 amino acid residues were lacking, including the \u3b1-helix. SCR\u394 had lost the scramblase activity and its affinity for Ca2+ was decreased by one order of magnitude. Fluorescence and IR spectroscopic studies revealed that the C-terminal region of SCR was essential for the proper folding of the protein. Moreover, it was found that Ca2+ exerted an overall destabilizing effect on SCR, which might facilitate its binding to membranes