Erratum — A 3D semantic segmentation network for accurate neuronal soma segmentation by Li Ma, Qi Zhong, Yezi Wang, Xiaoquan Yang, Qian Du

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SAMNet++: A Segment Anything Model for Supervised 3D Point Cloud Semantic Segmentation by Mohsen Shahraki, Ahmed Elamin, Ahmed El-Rabbany

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3D-SCUMamba: An Abdominal Tumor Segmentation Model by Juwita, Ghulam Mubashar Hassan, Amitava Datta

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Segmentation of glioblastomas via 3D FusionNet by Xiangyu Guo, Botao Zhang, Yue Peng, Feng Chen, Wenbin Li

Subjects: “…brain tumor segmentation…”
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Taming Mambas for 3D Medical Image Segmentation by Luca Lumetti, Vittorio Pipoli, Kevin Marchesini, Elisa Ficarra, Costantino Grana, Federico Bolelli

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Lightweight DeepLabv3+ for Semantic Food Segmentation by Bastián Muñoz, Angela Martínez-Arroyo, Constanza Acevedo, Eduardo Aguilar

Subjects: “…semantic food segmentation…”
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PASeg: positional-guided segmenter with multimodal semantic alignment for enhancing urban scene 3D semantic segmentation by Yang Luo, Ting Han, Xiaorong Zhang, Yujun Liu, Duxin Zhu, Jinyuan Li, Yiping Chen, Yundong Wu, Guorong Cai, Yingchao Piao, Jinhe Su

“…Concurrently, substantial progress has been made in 3D point cloud semantic segmentation, advancing the precision and effectiveness of urban scene interpretation. …”
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Steel surface defect segmentation with SME-DeeplabV3+ by Haiyan Zhang, Zining Zhao, Yilin Liu, Jiange Liu, Tingmei Ma, Kexiao Wu, Zhiwen Zhuang, Jiajun Wang

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War, traffic and iatrogenic injuries of D3 duodenal segment by Ignjatović Dragan, Ćuk Vladimir, Mišović Sidor

“…Case reports. All three cases were with the injuries of D3 duodenal segment. …”
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3D mesh segmentation of historic buildings for architectural surveys by Borja Javier Herráez, Eduardo Vendrell

“…Advances in three-dimensional (3D) acquisition systems have introduced this technology to more fields of study, such as archaeology or architecture. …”
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Object Segmentation and Ground Truth in 3D Embryonic Imaging. by Bhavna Rajasekaran, Koichiro Uriu, Guillaume Valentin, Jean-Yves Tinevez, Andrew C Oates

“…Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.…”
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CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy by Cyril Achard, Timokleia Kousi, Markus Frey, Maxime Vidal, Yves Paychere, Colin Hofmann, Asim Iqbal, Sebastien B Hausmann, Stéphane Pagès, Mackenzie Weygandt Mathis

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Efficient microstructure segmentation in three-dimensional imaging: Combining few-shot learning with the segment anything modelEarth/Chem by Po-Yen Tung, Richard J. Harrison

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Instance Segmentation Model Created from Three Semantic Segmentations of Mask, Boundary and Centroid Pixels Verified on GlaS Dataset by Peter Malík, Kristína Knapová, Štefan Krištofík

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Plant stem and leaf segmentation and phenotypic parameter extraction using neural radiance fields and lightweight point cloud segmentation networks by Gaofei Qiao, Zhibin Zhang, Bin Niu, Sijia Han, Enhui Yang

Subjects: “…three-dimensional point cloud…”
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Three-Blind Validation Strategy of Deep Learning Models for Image Segmentation by Andrés Larroza, Francisco Javier Pérez-Benito, Raquel Tendero, Juan Carlos Perez-Cortes, Marta Román, Rafael Llobet

“…To address this, we propose a novel validation framework—referred to as the three-blind validation strategy—that enables rigorous assessment of segmentation models in contexts where subjectivity and label variability are significant. …”
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3D-NASE: A Novel 3D CT Nasal Attention-Based Segmentation Ensemble by Alessandro Pani, Luca Zedda, Davide Antonio Mura, Andrea Loddo, Cecilia Di Ruberto

Subjects: “…3D CT segmentation…”
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Three-Dimensional Instance Segmentation of Rooms in Indoor Building Point Clouds Using Mask3D by Michael Brunklaus, Maximilian Kellner, Alexander Reiterer

Subjects: “…Mask3D…”
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3DLST: 3D Learnable Supertoken Transformer for LiDAR point cloud scene segmentation by Dening Lu, Linlin Xu, Jun Zhou, Kyle (Yilin) Gao, Jonathan Li

“…However, there is still considerable scope for further development in effective and efficient Transformers for large-scale LiDAR point cloud scene segmentation. This paper proposes a novel 3D Transformer framework, named 3D Learnable Supertoken Transformer (3DLST). …”
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ES-UNet: efficient 3D medical image segmentation with enhanced skip connections in 3D UNet by Minyoung Park, Seungtaek Oh, Junyoung Park, Taikyeong Jeong, Sungwook Yu

Subjects: “…3D medical image segmentation…”
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