Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnex... by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

“…Key Points We developed an image segmentation model to automatically delineate adnexal masses. …”
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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…Transformers are a promising deep learning approach for automated medical image segmentation. ObjectiveThis scoping review will synthesize current literature and assess the use of various transformer models for neuroimaging segmentation. …”
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Investigation of 3D iris morphology early alteration after implantable collamer lens implantation by using SS-OCT by Zhengwei Zhang, Shuimiao Chang, Kaiwen Li, Jinhan Yao, Zhao Wang, Jiewei Liu, Lin Jiang, Feiyan Chai, Xiaogang Wang

“…A multitask network automatically performed iris SS-OCT image segmentation and quantitative measurements of 3D iris morphology (iris thickness and volume of the inner 1-mm annular area and the outer 1–2-mm annular area, iris curvature [I-Curve], and iris smooth index [SI]). …”
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Radiomics for Predicting the Development of Brain Edema from Normal-Appearing Early Brain-CT After Cardiac Arrest and Return of Spontaneous Circulation by Michael Scheschenja, Eva-Marie Müller-Stüler, Simon Viniol, Joel Wessendorf, Moritz B. Bastian, Jarmila Jedelská, Alexander M. König, Andreas H. Mahnken

“…Patients without acute intracranial pathology on initial scans and who underwent follow-up brain CTs within 14 days post-return of spontaneous circulation (ROSC) were included. Image segmentation involved manual basalganglia segmentation and automated whole-brain segmentation. …”
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Multi-Class Guided GAN for Remote-Sensing Image Synthesis Based on Semantic Labels by Zhenye Niu, Yuxia Li, Yushu Gong, Bowei Zhang, Yuan He, Jinglin Zhang, Mengyu Tian, Lei He

“…Experiments show that MCGGAN can be effectively used as a data augmentation approach to improve the performance of downstream remote-sensing image segmentation tasks.…”
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Low-cost male urogenital simulator for penile implant surgery training: a 3D printing approach by Zixi Wang, Carlo Saija, Nicholas Raison, Abdullatif Aydin, Zhouyang Xu, Katie Zuo, Kawal Rhode, Antonia Pontiki

“…Methods Utilizing image segmentation of patient pre-operative computed tomography (CT) scans, combined with three-dimensional (3D) printing and silicone molding techniques, we developed a high-fidelity simulator replicating the anatomical structures of the male urogenital system. …”
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A multi-modal multi-branch framework for retinal vessel segmentation using ultra-widefield fundus photographs by Qihang Xie, Qihang Xie, Xuefei Li, Yuanyuan Li, Yuanyuan Li, Jiayi Lu, Jiayi Lu, Shaodong Ma, Yitian Zhao, Yitian Zhao, Jiong Zhang, Jiong Zhang

“…Complementing this, the Attention-Guided Upsampling Module (AUM) enhances segmentation performance through convolution operations guided by attention mechanisms.ResultsExtensive experimental evaluations demonstrate that our approach significantly outperforms existing state-of-the-art methods for UWF fundus image segmentation.…”
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Three-dimensional structure of entire hydrated murine hearts at histological resolution by Jasper Frohn, Frederik Böddeker, Marius Reichardt, Hendrik Bruns, Titus Czajka, Amara Khan, Ludovic Broche, Michael Krisch, Alberto Bravin, Frauke Alves, Jana Zschüntzsch, Tim Salditt

“…All datasets have been analyzed using semi-automated image segmentation of the ventricles, rotation into a common coordinate system, classification into different anatomical compartments, and finally the structure tensor approach. …”
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Automatic MRI Lymph Node Annotation From CT Labels by Souraja Kundu, Yuji Iwahori, M. K. Bhuyan, Manish Bhatt, Boonserm Kijsirikul, Aili Wang, Akira Ouchi, Yasuhiro Shimizu

“…It also has broader applications in multimodality image segmentation. We open-source the code through a GitHub repository.INDEX TERMS Image registration, image annotation, magnetic resonance imaging, computed tomography, unsupervised learning, superresolution.…”
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Retinopathy Disease Detection and Classification Using a Coordinate Attention Module-Based Convolutional Neural Network with Leaky Rectified Linear Unit by Pravin Balaso Chopade, Prabhakar N. Kota, Bhagvat D. Jadhav, Pravin Marotrao Ghate, Shriram Sadashiv Kulkarni

“…The preprocessed images are given to Mayfly Optimization Algorithm-based Region Growing (MOARG) for image segmentation. Then, the features are extracted using ResNet50 and SqueezeNet, which extract deep learning features. …”
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Automatic etiological classification of stroke thrombus digital photographs using a deep learning model by Álvaro Lucero-Garófano, Álvaro Lucero-Garófano, Alicia Aliena-Valero, Isabel Vielba-Gómez, Isabel Vielba-Gómez, Irene Escudero-Martínez, Irene Escudero-Martínez, Lluís Morales-Caba, Lluís Morales-Caba, Fernando Aparici-Robles, Fernando Aparici-Robles, Diana L. Tarruella Hernández, Diana L. Tarruella Hernández, Gerardo Fortea, Gerardo Fortea, José I. Tembl, José I. Tembl, Juan B. Salom, Juan B. Salom, José V. Manjón

“…A DL method was designed based on two deep neural networks for: (1) image segmentation and (2) image classification including clinical characteristics. …”
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Analysis of stomatal characteristics of maize hybrids and their parental inbred lines during critical reproductive periods by Changyu Zhang, Changyu Zhang, Yu Jin, Yu Jin, Jinglu Wang, Jinglu Wang, Ying Zhang, Ying Zhang, Yanxin Zhao, Xianju Lu, Xianju Lu, Wei Song, Xinyu Guo, Xinyu Guo

“…We uesd a target detection and image segmentation approach based on YOLOv5s and Unet to efficiently obtain 11 phenotypic traits encompassing stomatal count, shape, and distribution. …”
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SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans by Mehmet Akif Cifci

“…Due to the low contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor in CT images is challenging. …”
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Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming by Yan Guo, Jin Zhang, Chengxin Yin, Xiaonan Hu, Yu Zou, Zhipeng Xue, Wei Wang

“…Firstly, the region proposal network (RPN) is utilized to recognize and localize the leaves in complex surroundings. Then, images segmented based on the results of RPN algorithm contain the feature of symptoms through Chan–Vese (CV) algorithm. …”
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Application of improved and efficient image repair algorithm in rock damage experimental research by Mingzhe Xu, Xianyin Qi, Diandong Geng

“…The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. …”
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Sex differences in normative modeling of cortical thickness in cannabis use disorder by Erin L. Martin, Laura M. Campbell, Kathryn Thorn, Gregory L. Sahlem, Aimee L. McRae-Clark, Andreana Benitez

“…Here, we assessed sex differences in brain regions measured via 3 T MRI in 72 adults (50 males, 22 females) with CUD. T1-weighted images, segmented via FreeSurfer, were used to derive Normative Morphometry Imaging Statistics z-scores (accounting for age, sex, intracranial volume, and image quality). …”
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Automated quantification of axonal and myelin changes in contusion, dislocation, and distraction spinal cord injuries: Insights into targeted remyelination and axonal regeneration by Xuan Li, Yuan He, Fangyao Chen, Xin Tong, Yunlong Fan, Yuzhe Langzhou, Jie Liu, Kinon Chen

“…Spinal cords were processed and stained for neurofilament, tubulin, and myelin basic protein, with histology images segmented into dorsal, lateral, and ventral white matter regions. …”
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A Transfer Learning Method to Generate Synthetic Synoptic Magnetograms by Xiaoyue Li, Valliappan Senthamizh Pavai, Daria Shukhobodskaia, Mark D. Butala, Luciano Rodriguez, Jasmina Magdalenic, Véronique Delouille

“…Our proposed model is trained with only 528 paired image segments and enforces a reliable data division strategy.…”
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Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images by Xiaoning Zhang, Yi Yu, Daqun Li, Yuqing Wang

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