Automated Histological Segmentation on Micro Computed Tomography Images of Atherosclerotic Arteries

Currently, the treatment of peripheral arterial obstructive disease (PAOD) based on plaque morphology is still debated;1Conte M.S. Bradbury A.W. Kolh P. White J.V. Dick F. Fitridge R. et al.Global Vascular Guidelines management chronic limb-threatening ischemia.Eur J Vasc Endovasc Surg. 2019; 58 (e33): S1-S109Abstract Full Text PDF PubMed Scopus (339) Google Scholar one main reasons for this a lack knowledge about specific histological components in patient with PAOD. Micro computed tomography (micro-CT) offers better spatial resolution than standard clinical CT, and has been proven to be valuable tool recognition different atherosclerotic components.2Jinnouchi H. Torii S. Kutyna M. Sakamoto A. Kolodgie F.D. Finn A.V. al.Micro–computed demonstration multiple ruptures single individual presenting sudden cardiac death.Circ Cardiovasc Imaging. 2018; 11e008331Crossref (4) Combining micro-CT images an artificial intelligence (AI) algorithm trained perform image segmentation, aim was prove feasibility automated histopathological segmentation popliteal artery (PA) images. PAs were removed from amputated limbs six patients PAOD assessed by histology (using Movat haematoxylin eosin staining). This study approved ethics committee at University Hospital Strasbourg (2018-A03406-49 RIPH3). All co-registered sections cross PA annotated, using classes, expert. Each pixel assigned class each annotated expert possessed corresponding slide. Six classes chosen: four (“fibrous tissue”, “lipid pools”, “sheet calcifications”, “nodular calcifications”) two other (“background lumen” “specimen holder”). Deep learning through convolutional neural network (CNN) U-net architecture used, supplementing usual contracting second where pooling operations replaced upsampling operators, thus increasing outputs. A final, fully connected layer then produced information. CNN pairs histologically images, which constituted training set. The goal learn weights bias parameters network. accuracy controlled test set, consisted their annotations, held apart during whole process order not introduce any training. Confusion matrices compared repartition labels between ground truth annotations predictions. area under curve (AUC) calculated vs. all strategy. Dice scores measured similarity actual predicted Three dimensional (3D) reconstructions rendered approach feeding outputs 3D probabilistic model.3Kamnitsas K. Ledig C. Newcombe V.F.J. Simpson J.P. Kane A.D. Menon D.K. al.Efficient multi-scale CRF accurate brain lesion segmentation.Med Image Anal. 2017; 36: 61-78Crossref (1625) total 91 examined sections. set included 81 (out 91). Data augmentation techniques used (rotations, rescaling, gamma correction) build final 1 620 elements. remaining 10 original slices leading 200 after data augmentation. 0.99 background, 0.94 fibrous tissues, 0.86 specimen holder, 0.85 sheet calcifications, 0.64 nodular 0.41 lipid pools. AUC precision recall curves background highest (1.00). tissue high (0.96). Calcifications also accurately detected, although calcifications easier detect (AUC = 0.90) calcification 0.61). holder detection fairly 0.86). harder pools, 0.23. Also, rendering possible (Fig. 1). In study, work building system able segment automatically demonstrated. novelty lies fact that use AI deep established histopathology PAOD, therefore allowing broadening method differentiate lesions, gathering information plaques plaque–device interactions, more detailed It demonstrated can tissue, distinguish render reconstructions. Automated will reduce analysis time, decrease workload, improve reproducibility accuracy. Pathologists analyse ex vivo arteries three dimensions, volume approach, guide them towards region interest help decide whether further without destroying specimen. Having diagnosis characterisation are necessary identify lesions needs treatment, improving care, as recent study,4Torii Jinnouchi Mori Park J. Amoa F.C. al.Vascular responses coronary following implantation newer-generation drug-eluting stents humans: impact healing.Eur Heart 2020; 41: 786-796Crossref (22) showed severe stented caused delayed healing lesion. might applied CT near future owing extensive co-registration micro-CT, analysis,5Kuntz Cornelissen Sato Y. al.Co-registration conventional CT-angiography, CLTI patients.Eur 61: 146-154Abstract (3) potential register characteristics pre-operatively (in situ histology) clinicians decision making process. Micro-CT increase ability refining its border, treatment.