A modified segmentation method for determination of IV vessel boundaries

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

“…For the segmentation of the coronary walls, various AIbased techniques, such as ML-based or DL-based, have been applied. The ML-based method includes XGBoost [79,107,108], k-means [43], hidden Markov random field (HMRF) [43,109,110], support vector machine (SVM) [65,82], random forest (RF) [65,82], fuzzy c-means (FCM) [43,89], Pix2Pix model [74], ellipse-fitting algorithm [28], Lucky-Richardson algorithm [84], and gradient boosting [85]. The DL-based method includes generative adversarial network (GAN) [74], convolutional neural network (CNN) [78,81,95], bidirectional gated recurrent unit (Bi-GRU) [74], efficient net [75], DeepLabV3 [80], location-adaptive threshold method (LATM) [111], scan-adaptive threshold method (SATM) [111], and fully convolutional neural network (FCNN) [87].…”

“…SVM, support vector machine; RF, random forest; HMRF, hidden Markov random field; CNN, convolutional neural networks; FCNN, fully convolutional neural network; GAN, generative adversarial network; Bi-GRU, bidirectional gated recurrent unit; LSTM, long short-term memory; LATM, location-adaptive threshold method; SATM, scan-adaptive threshold method. Conventional method: (i) Otsu thresholding [90], (ii) Fuzzy method [87,89], (iii) Parametric deformable model [92], (iv) Geometric deformable model [92], (v) Gradient vector flow (GVF) [94], (vi) K-means [43], (vii) Lucky Richard algorithm [84], (viii) Ellipse fitting algorithm [28]. Machine Learning: (i) SVM [65,82], (ii) XGBoost [79,107,108], (iii) RF [65,82], (iv) Gradient boosting [85], (v) HMRF [43,109,110].…”

“…The three types of bias employed in the VD process are shown in Figure 19a,c, including low bias (Figure 19a), moderate bias (Figure 19b), and high bias (Figure 19c). The number of studies in low bias (out of 28 studies) for RBM, RBA, and RBS was three (10%) [48,58,62], eight (28%) [45,49,50,57,60,61,64,69], and four (14%) [24,48,65,67] respectively, whereas the number of studies under low bias (out of 60 studies) for RBM, RBA, and RBS was seven (12%) [58,75,79,81,82,86,87], twelve (20%) [28,65,77,84,85,[89][90][91][92][93][94]111], and seven (12%) [24,48,55,58,59,62,67], respectively. Out of 28 and 60 studies, respectively, 2 [48,…”

“…The studies that fell under the intersection of (RBA, RBM, RBS), (RBA, RBM), and (RBM, RBS) was two, five, and six, respectively, whereas no common studies were found under the intersection of (RBA, RBS). Moreover, the number of studies under high bias (out of 60 studies) for RBM, RBA, and RBS was 44 (73%) [24,28,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][59][60][61][62][64][65][66][67][68][69][70][71][72][73][74]77,84,85,[88][89][90][91][92][93][94]97,100,111], 28 (46%) [24,43,[45][46]...…”