Automatic Segmentation for Intracoronary OCT Image Based on Convolutional Neural Network and Support Vector Machine Methods

Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(IVUS)were unable to identify vulnerable plaques due to their limited resolution.Fortunately,optical coherence tomography(OCT)is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque.In particular,it has the ability to identify plaques with fibrous cap thickness<65μm,an accepted threshold value for vulnerable plaques.However,segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective.To overcome time consumption,several methodologies have been proposed for automatic segmentation of OCT images but most of these methods were still limited by intricate image preprocessing and expensive computation.In this research,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque components.Methods In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained.OCT were obtained from ILUMIEN OPTIS System(St.Jude,Minnesota,MN).The OCT catheter was traversed to the segment of interest and the catheter pullback was limited at a rate of 20 mm/sec.Following the OCT image acquisition,the IVUS catheter was traversed distally though the artery to the same coronary segment(Volcano Therapeutics,Rancho Cordova)and the catheter pullback speed was at a standard rate of 0.5 mm/sec.Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for our segmentation study.Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations.VH-IVUS was used as references and guide by the experts in the manual segmentation process.Three plaque component tissue classes were identified from OCT images in this work:lipid tissue(LT),fibrous tissue(FT)and background(BG).Procedures using two machine learning methods(CNN and SVM)were developed to segment OCT images,respectively.For CNN method,the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images.For SVM method,local binary patterns(LBPs),gray level co-occurrence matrices(GLCMs)which contains contrast,correlation,energy and homogeneity,entropy and mean value were calculated as features and assembled to feed SVM classifier.The accuracies of two segmentation methods were evaluated and compared using the OCT dataset.Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels.Results The overall classification accuracy based CNN method reached 95.8%,and the accuracies for LT,FT and BG were 86.8%,83.4%,and 98.2%,respectively.The overall classification accuracy based SVM was 71.9%,and per-class accuracy for LT,FT and BG was 75.4%,78.3%,and67.0%,respectively.Conclusions The two methods proposed can automatically identify plaque region and characterize plaque compositions for OCT images and potentially reduce the time spent by doctors in segmenting and evaluating coronary plaque OCT images.CNN provided better segmentation accuracies compared to those achieved by SVM.

Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification

Optical coherence tomography(OCT)is a new intravascular imaging technique with high resolution and could provide accurate morphological information for plaques in coronary arteries.However,its segmentation is still commonly performed manually by experts which is time-consuming.The aim of this study was to develop automatic techniques to characterize plaque components and quantify plaque cap thickness using 3 machine learning methods including convolutional neural network(CNN)with U-Net architecture,CNN with Fully convolutional DenseNet(FC-DenseNet)architecture and support vector machine(SVM).In vivo OCT and intravascular ultrasound(IVUS)images were acquired from two patients at Emory University with informed consent obtained.Eighteen OCT image slices which included lipid core and with acceptable image quality were selected for our study.Manual segmentation from imaging experts was used as the gold standard for model training and validation.Since OCT has limited penetration,virtual histology IVUS was combined with OCT data to improve reliability.A 3-fold cross-validation method was used for model training and validation.The overall tissue classification accuracy for the 18 slices studied(total classification database sample size was 8580096 pixels)was 96.36%and 92.72%for U-Net and FC-DenseNet,respectively.The best average prediction accuracy for lipid was 91.29%based on SVM,compared to 82.84%and 78.91%from U-Net and FC-DenseNet,respectively.The overall average accuracy(Acc)differentiating lipid and fibrous tissue were 95.58%,92.33%and 81.84%for U-Net,FC-DenseNet and SVM,respectively.The average errors of U-Net,FC-DenseNet and SVM from the 18 slices for cap thickness quantification were 8.83%,10.71%and 15.85%.The average relative errors of minimum cap thickness from 18 slices of U-Net,FC-DenseNet and SVM were 17.46%,13.06%and 22.20%,respectively.To conclude,CNN-based segmentation methods can better characterize plaque compositions and quantify plaque cap thickness on OCT images and are more likely to be used in the clinical arena.Large-scale studies are needed to further develop the methods and validate our findings.

Squalene epoxidase promotes colorectal cancer cell proliferation through accumulating calcitriol and activating CYP24A1-mediatedMAPK signaling

Background:Colorectal cancer(CRC)is one of the most malignant tumorswith high incidence,yet its molecular mechanism is not fully understood,hindering the development of targeted therapy.Metabolic abnormalities are a hallmark of cancer.Targeting dysregulated metabolic features has become an important direction for modern anticancer therapy.In this study,we aimed to identify a new metabolic enzyme that promotes proliferation of CRC and to examine the related molecular mechanisms.Methods:We performed RNA sequencing and tissue microarray analyses of human CRC samples to identify new genes involved in CRC.Squalene epoxidase(SQLE)was identified to be highly upregulated in CRC patients.The regulatory function of SQLE in CRC progression and the therapeutic effect of SQLE inhibitors were determined by measuring CRC cell viability,colony and organoid formation,intracellular cholesterol concentration and xenograft tumor growth.Themolecularmechanism of SQLE functionwas explored by combining transcriptome and untargeted metabolomics analysis.Western blotting and realtime PCR were used to assess MAPK signaling activation by SQLE.Results:SQLE-related control of cholesterol biosynthesis was highly upregulated in CRC patients and associated with poor prognosis.SQLE promoted CRC growth in vitro and in vivo.Inhibition of SQLE reduced the levels of calcitriol(active form of vitamin D3)and CYP24A1,followed by an increase in intracellular Ca2+concentration.Subsequently,MAPK signaling was suppressed,resulting in the inhibition of CRC cell growth.Consistently,terbinafine,an SQLE inhibitor,suppressed CRC cell proliferation and organoid and xenograft tumor growth.Conclusions:Our findings demonstrate that SQLE promotes CRC through the accumulation of calcitriol and stimulation of CYP24A1-mediated MAPK signaling,highlighting SQLE as a potential therapeutic target for CRC treatment.