Plant Detection in RGB Images from Unmanned Aerial Vehicles Using Segmentation by Deep Learning and an Impact of Model Accuracy on Downstream Analysis by Mikhail V. Kozhekin, Mikhail A. Genaev, Evgenii G. Komyshev, Zakhar A. Zavyalov, Dmitry A. Afonnikov

“…In the present work, a method is presented for detecting the plants of five species in images acquired via a UAV on the basis of image segmentation by deep learning algorithms (convolutional neural networks). …”
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Segmentasi Citra Wajah dengan Implementasi Adaptif Threshold- Integral Image by Maryam Ummul Habibah, Muchamad Kurniawan

“…And the best results are obtained, name the image in the room (indoor) the value of S = 1/2 and T = 50, and the image outside the room (outdoor) the value of S = 1/30 and T = 30. Face image segmentation using the Adaptive Threshold - Integral Image robust method of high and low light intensity by setting the S (kernel) and T (Threshold) parameter values, this method is able to split the face object and its background. …”
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Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibrationMendeley Data by Karla Gabriele Florentino da Silva, Paulo Victor de Magalhães Rozatto, Kaio de Oliveira e Sousa, Lucas Dias Hudson, Artur Welerson Sott Meyer, Alemilson Fabiano Silva, Igor Tibiriçá Mendes, Alex Rodrigues Borges, Leandro Elias Morais, Luiz Maurílio da Silva Maciel, Saulo Moraes Villela, Helio Pedrini, Marcelo Bernardes Vieira

“…We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. …”
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Deep Learning-Based Fully Automatic Segmentation of the Paranasal Sinuses in Chronic Rhinosinusitis Patients Using Computed Tomographic Images by Yuhang Wang, Xiaolei Zhang, Weidong Du, Na Dai, Yi Lyv, Keying Wu, Yiyang Tian, Yuxin Jie, Yu Lin, Weipiao Kang

“…We chose a custom 3D nnU-Net v2 network model because of its excellent performance in the field of 3D medical image segmentation, especially in automated training and accurate segmentation of complex structures. …”
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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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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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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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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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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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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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