Evaluation of Siemens Healthineers’ StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke by Lynnet-Samuel J. Teichmann, Ahmed A. Khalil, Kersten Villringer, Jochen B. Fiebach, Stefan Huwer, Eli Gibson, Ivana Galinovic

“…The performance of the StrokeSegApp was compared against this ground truth using the dice similarity coefficient (DSC) and Bland–Altman plots. …”
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Multi-scale channel attention U-Net: a novel framework for automated gallbladder segmentation in medical imaging by Yiming Zhou, Xiaobo Wen, Xiaobo Wen, Kang Fu, Meina Li, Lin Sun, Xiao Hu

“…Our proposed MCAU-Net model was employed for gallbladder segmentation and its performance was evaluated using Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), Positive Predictive Value (PPV), Sensitivity (SE), Hausdorff Distance (HD), Relative Volume Difference (RVD), and Volumetric Overlap Error (VOE) metrics.ResultsOn the test set, MCAU-Net achieved DSC, JSC, PPV, SE, HD, RVD, and VOE values of 0.85 ± 0.22, 0.79 ± 0.23, 0.92 ± 0.14, 0.84 ± 0.23, 2.75 ± 0.98, 0.18 ± 0.48, and 0.22 ± 0.42, respectively. …”
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Seguridad en el uso de maquinaria agropecuaria: conductas y prácticas de los productores rurales de las provincias argentinas de Santa Fe y Córdoba by M. GRIGIONI, F. DONÁ, M. BONINO

“…El 71% de los entrevistados dice tener en cuenta los dispositivos de seguridad que tiene una máquina al momento de comprarla y el 41% controla las medidas de seguridad con que los contratistas trabajan en sus propiedades. …”
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CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans by Raffaella Fiamma Cabini, Andrea Cozzi, Svenja Leu, Benedikt Thelen, Rolf Krause, Filippo Del Grande, Diego Ulisse Pizzagalli, Stefania Maria Rita Rizzo

“…Two U-nets were used to segment the axial slices, with performance evaluated through the volumetric Dice similarity coefficient (vDSC). CompositIA’s performance in quantifying body composition indices was assessed using mean percentage relative error (PRE), regression, and Bland–Altman analyses. …”
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Universal conditional networks (UniCoN) for multi-age embryonic cartilage segmentation with Sparsely annotated data by Nishchal Sapkota, Yejia Zhang, Zihao Zhao, Maria Jose Gomez, Yuhan Hsi, Jordan A. Wilson, Kazuhiko Kawasaki, Greg Holmes, Meng Wu, Ethylin Wang Jabs, Joan T. Richtsmeier, Susan M. Motch Perrine, Danny Z. Chen

“…On average, we achieve a 1.7% Dice score increase with minimal computational overhead and a 7.5% improvement on unseen data. …”
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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

“…Testing results demonstrated that the model accurately identified the segmentation areas, achieving a Dice Similarity Coefficient of 92.8%, Intersection over Union of 86.64%, accuracy of 99.69%, precision of 92.63%, and recall of 93.22%. …”
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Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up by Loïse Dessoude, Raphaëlle Lemaire, Romain Andres, Thomas Leleu, Alexandre G. Leclercq, Alexis Desmonts, Typhaine Corroller, Amirath Fara Orou-Guidou, Luca Laduree, Loic Le Henaff, Joëlle Lacroix, Alexis Lechervy, Dinu Stefan, Aurélien Corroyer-Dulmont

“…There was a high degree of overlap between the AI and the doctor's segmentation, with a mean DICE score of 0.77. The diameter and volume of the BM lesions were found to be concordant between the AI and the reference segmentation. …”
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A novel multimodality anthropomorphic phantom enhances compliance with quality assurance guidelines for magnetic resonance imaging in radiotherapy by Meshal Alzahrani, David A Broadbent, Irvin Teh, Bashar Al-Qaisieh, Emily Johnstone, Richard Speight

“…Both phantoms achieved target registration errors (TREs) below 0.97 mm and dice similarity coefficient (DSC) values above 0.9, meeting guidelines. …”
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Proof of concept of fully automated adaptive workflow for head and neck radiotherapy treatments with a conventional linear accelerator by Gaia Muti, Marco M. J. Felisi, Angelo F. Monti, Chiara Carsana, Roberto Pellegrini, Edoardo Salmeri, Mauro Palazzi, Paola E. Colombo

“…An analysis of the timing for the different steps is carried out to assess its clinical applicability.ResultThe dice of the five HU layer structures range between 0.66 and 0.99. …”
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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

“…Results The FCN ResNet101 demonstrated the highest segmentation performance for adnexal masses (Dice similarity coefficient: 89.1%). Support vector machine achieved the best AUC (0.961, 95% CI: 0.925–0.996). …”
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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

“…The most frequent evaluation metric was the Dice score (n=63, 94.03%). Studies generally reported increased segmentation accuracy and the ability to model both local and global features in brain images. …”
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Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation by Tristan Whitmarsh, Wei Cope, Julia Carmona-Bozo, Roido Manavaki, Stephen-John Sammut, Ramona Woitek, Elena Provenzano, Emma L. Brown, Sarah E. Bohndiek, Ferdia A. Gallagher, Carlos Caldas, Fiona J. Gilbert, Florian Markowetz

“…Results Using two-way cross-validation, we show that vessels were accurately segmented, with Dice scores of 0.875 and 0.856, and were accurately identified, with F1 scores of 0.777 and 0.748. …”
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Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor by Hassan Bagher-Ebadian, Stephen L. Brown, Mohammad M. Ghassemi, Prabhu C. Acharya, Indrin J. Chetty, Benjamin Movsas, James R. Ewing, Kundan Thind

“…The K-SOM PNMS’s estimation for the leaky tumor regions were strongly similar (Dice-Similarity-Coefficient, DSC = 0.774 [CI: 0.731–0.823], and 0.866 [CI: 0.828–0.912] for Models 2 and 3, respectively) to their respective NMS regions. …”
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A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography by Malin Kristin Meier, Ramon Andreas Helfenstein, Adam Boschung, Andreas Nanavati, Adrian Ruckli, Till D. Lerch, Nicolas Gerber, Bernd Jung, Onur Afacan, Moritz Tannast, Klaus A. Siebenrock, Simon D Steppacher, Florian Schmaranzer

“…Model performance was assessed with six evaluation metrics including Dice similarity coefficient (DSC). In addition, model performance was tested on an external dataset (40 patients) with a 3D T2-weighted sequence from a different institution. …”
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Preliminary application of a cervical vertebra segmentation method based on Transformer and diffusion model for lateral cephalometric radiographs in orthodontic clinical practice by LIU Yang, WU Mengyi, HU Yao, QI Kun, WANG Yubin, ZHAO Yue, SONG Jinlin

“…The segmentation performance was quantitatively evaluated by two metrics, Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), and also qualitatively assessed through physicians' manual annotations and model visualization results.Results·The cervical vertebra segmentation method based on Transformer and diffusion models achieved DSC and IoU scores of 93.3% and 87.5%, respectively, significantly outperforming the U-Net and SOLOv2 methods (with improvements of 3.0% and 4.1% in DSC, and 5.2% and 7.1% in loU, respectively). …”
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Automated chest CT three-dimensional quantification of body composition: adipose tissue and paravertebral muscle by Akinori Hata, Yohei Muraguchi, Minoru Nakatsugawa, Xinan Wang, Jiyeon Song, Noriaki Wada, Takuya Hino, Kota Aoyagi, Masami Kawagishi, Takuo Negishi, Vladimir I. Valtchinov, Mizuki Nishino, Akihiro Koga, Naoki Sugihara, Masahiro Ozaki, Gary M. Hunninghake, Noriyuki Tomiyama, Mark L. Schiebler, Yi Li, David C. Christiani, Hiroto Hatabu

“…The AI algorithm was trained on the training sets, and its performance was evaluated on the test sets. The AI achieved Dice scores above 0.87 and showed excellent correlations for VAT/SAT ratios, muscle attenuation value, and IMAT% (correlation coefficients > 0.98, p < 0.001). …”
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Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer by Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen

“…CLIP-UNet with cluster information achieved a Dice score of 86.2% compared to 84.4% from the baseline UNet. …”
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Whole-body tumor segmentation from FDG-PET/CT: Leveraging a segmentation prior from tissue-wise projections by Sambit Tarai, Elin Lundström, Nouman Ahmad, Robin Strand, Håkan Ahlström, Joel Kullberg

“…All the methods were independently evaluated using 5-fold cross-validation on the autoPET dataset and subsequently tested on the U-CAN dataset.Results:Combining the segmentation prior with the original SUV and CT images improved overall tumor segmentation performance significantly compared to a baseline network. The increase in Dice coefficient for lymphoma, lung cancer, and melanoma across different segmentation networks were: 3D UNet (0.04⁎, 0.02⁎, 0.11⁎), dynUNet (0.05⁎, 0.04⁎, 0.08⁎), and nnUNet (0.02⁎, 0.00ns, 0.03⁎), respectively; *, p-value < 0.05; ns, non-significance.Conclusion: The increased segmentation accuracy could be attributed to the segmentation prior generated from tissue-wise SUV projections, revealing information from various tissues that was useful for segmentation of tumors. …”
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Investigating the potential of diffusion tensor atlases to generate anisotropic clinical tumor volumes in glioblastoma patients by Kim Hochreuter, Gregory Buti, Ali Ajdari, Christopher P. Bridge, Gregory C. Sharp, Sune Jespersen, Slávka Lukacova, Thomas Bortfeld, Jesper F. Kallehauge

“…The similarity between patient- and atlas-DTI CTVs was analyzed using the Dice Similarity Coefficient (DSC), with significance testing according to a Wilcoxon test. …”
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Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation by Lana Wang, Zhenyu Yang, Zhenyu Yang, Dominic LaBella, Zachary Reitman, John Ginn, Jingtong Zhao, Justus Adamson, Kyle Lafata, Kyle Lafata, Kyle Lafata, Evan Calabrese, John Kirkpatrick, Chunhao Wang

“…Regarding segmentation performance, SPU-Net demonstrated comparable results to a traditional U-Net in sensitivity (0.758 vs. 0.746), Dice similarity coefficient (0.760 vs. 0.742), reduced mean Hausdorff distance (mHD) (0.612 cm vs 0.744 cm), and reduced 95% Hausdorff distance (HD95) (2.682 cm vs 2.912 cm). …”
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