AESR3D: 3D overcomplete autoencoder for trabecular computed tomography super resolution by Shuwei Zhang, Yefeng Liang, Xingyu Li, Shibo Li, Xiaofeng Xiong, Lihai Zhang

“…A deep learning model AESR3D is proposed to recover high‐resolution (HR) Micro‐CT from low‐resolution Micro‐CT and implement an unsupervised segmentation for better trabecular observation and measurement. …”
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Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists by Michael Y. Chen, Maria A. Woodruff, Prokar Dasgupta, Nicholas J. Rukin

Subjects: “…3D printing…”
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MDMU-Net: 3D multi-dimensional decoupled multi-scale U-Net for pancreatic cancer segmentation by Lian Lu, Miao Wu, Gan Sen, Fei Ren, Tao Hu

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Crops3D: a diverse 3D crop dataset for realistic perception and segmentation toward agricultural applications by Jianzhong Zhu, Ruifang Zhai, He Ren, Kai Xie, Aobo Du, Xinwei He, Chenxi Cui, Yinghua Wang, Junli Ye, Jiashi Wang, Xue Jiang, Yulong Wang, Chenglong Huang, Wanneng Yang

“…It stands as the pioneering dataset that comprehensively supports the three critical tasks in 3D crop phenotyping: instance segmentation of individual plants in agricultural settings, plant type perception, and plant organ segmentation. …”
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WCN24-1132 ACTION3 – Phase 3 study of DMX-200 for the treatment of focal segmental glomerulosclerosis (FSGS) by Jonathon Barratt, Anant Desai, Alessia Fornoni, Lesley Inker, Irene de Lourdes Noronha, Alejandra Quevedo, Robert Shepherd, Alisha Smith, Hernan Trimarchi, Lucila Valente, Mona Wahba, Muh Geot Wong, Hiddo Lambers Heerspink

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A Resource-Efficient 3D U-Net for Hippocampus Segmentation Using CLAHE and SCE-3DWT Techniques by Faizaan Fazal Khan, Jun-Hyung Kim, Chun-Su Park, Ji-In Kim, Goo-Rak Kwon

Subjects: “…selective coefficient-enhanced 3D wavelet transform for MRI segmentation (SCE-3DWT)…”
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Comparison of anterior chamber depth measured by three different anterior segment analysis systems by Han Song, Wei Yang, Chunliu Yang, Qing Sun

“…ACD was measured using three anterior segment analysis systems: Pentacam, Sirius, and IOLMaster 700. …”
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Identification of Alcoholism Based on Wavelet Renyi Entropy and Three-Segment Encoded Jaya Algorithm by Shui-Hua Wang, Khan Muhammad, Yiding Lv, Yuxiu Sui, Liangxiu Han, Yu-Dong Zhang

“…This study proposes a novel computer-vision-based method for automatic detection of AUD based on wavelet Renyi entropy and three-segment encoded Jaya algorithm from MRI scans. …”
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Root segmentation of horticultural plants in X-Ray CT images by integrating 2D instance segmentation with 3D point cloud clustering by Mary E. Cassity, Paul C. Bartley, Yin Bao

“…A pretrained Mask R-CNN model was fine-tuned on images selected along different axes of the three-dimensional scans to identify the best view selection strategy for volumetric root segmentation. …”
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Three-Dimensional Landing Zone Segmentation in Urbanized Aerial Images from Depth Information Using a Deep Neural Network–Superpixel Approach by N. A. Morales-Navarro, J. A. de Jesús Osuna-Coutiño, Madaín Pérez-Patricio, J. L. Camas-Anzueto, J. Renán Velázquez-González, Abiel Aguilar-González, Ernesto Alonso Ocaña-Valenzuela, Juan-Belisario Ibarra-de-la-Garza

Subjects: “…three-dimensional semantic segmentation…”
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The 3D tooth model segmentation method based on GAC+PointMLP network by Jianjun Chen, Liyuan Zheng, Huilai Zou, Jiafa Mao, Weiguo Sheng

Subjects: “…Tooth segmentation…”
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GCF-DeepLabv3+: An Improved Segmentation Network for Maize Straw Plot Classification by Yuanyuan Liu, Jiaxin Zhang, Yueyong Wang, Yang Luo, Pengxiang Sui, Ying Ren, Xiaodan Liu, Jun Wang

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Semantic Segmentation Using Lightweight DeepLabv3+ for Desiccation Crack Detection in Soil by Hui Yean Ling, See Hung Lau, Siaw Yah Chong, Min Lee Lee, Yasuo Tanaka

Subjects: “…semantic segmentation…”
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Deep learning segmentation of soil constituents in 3D X-ray CT images by Maxime Phalempin, Lars Krämer, Maik Geers-Lucas, Fabian Isensee, Steffen Schlüter

“…We evaluated nnUNet on three challenging datasets: (1) complex soil structure with numerous material classes with overlapping gray value ranges, (2) fine roots in noisy images, and (3) permafrost with gradual gray value transitions between sediment types. …”
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3D-CNN with Multi-Scale Fusion for Tree Crown Segmentation and Species Classification by Jiayao Wang, Zhen Zhen, Yuting Zhao, Ye Ma, Yinghui Zhao

“…The study consists of two main processes: (1) combining semantic segmentation algorithms (U-Net and Deeplab V3 Plus) with watershed transform (WTS) for tree crown detection (U-WTS and D-WTS algorithms); (2) resampling the original images to different pixel densities (16 × 16, 32 × 32, and 64 × 64 pixels) and inputting them into five 3D-CNN models (ResNet10, ResNet18, ResNet34, ResNet50, VGG16). …”
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GRSNet: An Ultra-Lightweight Neural Network for 3D Point Cloud Classification and Segmentation by Zourong Long, Gen Tan, You Wu, Hong Yang, Chao Ding

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PSHop: A Lightweight Feed-Forward Method for 3D Prostate Gland Segmentation by Yijing Yang, Vasileios Magoulianitis, Jiaxin Yang, Jintang Xue, Masatomo Kaneko, Giovanni Cacciamani, Andre Abreu, Vinay Duddalwar, C.-C. Jay Kuo, Inderbir S. Gill, Chrysostomos Nikias

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A Lightweight Semantic Segmentation Model for Underwater Images Based on DeepLabv3+ by Chongjing Xiao, Zhiyu Zhou, Yanjun Hu

“…To address these challenges, this study proposes a lightweight semantic segmentation model based on DeepLabv3+. The framework employs MobileOne-S0 as the lightweight backbone for feature extraction, integrates Simple, Parameter-Free Attention Module (SimAM) into deep feature layers, replaces global average pooling in the Atrous Spatial Pyramid Pooling (ASPP) module with strip pooling, and adopts a content-guided attention (CGA)-based mixup fusion scheme to effectively combine high-level and low-level features while minimizing parameter redundancy. …”
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PA and PA-X: two key proteins from segment 3 of the influenza viruses by Xin Zhang, Xin Zhang, Yingying Tao, Yingying Tao, Li Wu, Jianhong Shu, Jianhong Shu, Yulong He, Yulong He, Huapeng Feng, Huapeng Feng

“…Previous studies have revealed that influenza virus segment 3 codes for not only the PA protein but also a novel protein, PA-X. …”
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Simulation of the of the DeepLabv3 neural network learning process for the agricultural fields segmentation by A. F. Rogachev, I. S. Belousov

“…Monitoring and determining the state of crops in agricultural production requires the use and improvement of neural network methods of artificial intelligence.The aim of the study is to create a mathematical model of the learning process of the DeepLabV3 neural network for intelligent analysis and segmentation of agricultural fields.Method. …”
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