Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence by Wang Wenhui, Gong Yitang, Chen Bingxian, Guo Hualei, Wang Qiang, Li Jing, Jin Cheng, Gui Kun, Chen Hao

“…The Ki67 quantitative analysis system was assessed on the validation set. The Dice coefficient of the tumor region segmentation model was 0.848, the Average Precision index of the nucleus detection model was 0.817, and the accuracy of the nucleus classification model was 96.66%. …”
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Segmentación del hígado en imágenes de tomografía computarizada by Melanie Yusta Gómez, Marlen Pérez Díaz, Rubén Orozco Morales, Xiomara Plasencia Hernández

“…Se realizó un análisis evaluativo y estadístico de los resultados obtenidos en la segmentación de las imágenes a partir de los coeficientes de Dice, Vinet y Jaccard.<br /><strong>Resultados:</strong> con el método Graph Cut, en todos los casos, se segmentó la región deseada, incluso cuando la calidad de las imágenes era baja, se observó gran similitud entre la imagen segmentada y la máscara de referencia. …”
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Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis by Ioannis Stathopoulos, Roman Stoklasa, Maria Anthi Kouri, Georgios Velonakis, Efstratios Karavasilis, Efstathios Efstathopoulos, Luigi Serio

“…We present the segmentation results for both the whole abnormal volume and for each abnormal component inside the examinations of the validation set. In the first case, a dice score coefficient (DSC), sensitivity, and precision of 0.76, 0.78, and 0.82, respectively, were found, while in the second case the model detected and segmented correct (True positives) the 48.8% (DSC ≥ 0.5) of abnormal components, partially correct the 27.1% (0.05 > DSC > 0.5), and missed (False Negatives) the 24.1%, while it produced 25.1% False Positives. …”
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Characterization of adrenal glands on computed tomography with a 3D V-Net-based model by Yuanchong Chen, Yaofeng Zhang, Xiaodong Zhang, Xiaoying Wang

“…Methods A total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance. …”
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Percepción de violencia en el noviazgo: un acercamiento a su análisis en estudiantes de medicina by Yamila Ramos Rangel, Laura López Angulo, María Suz Pompa, Daniela García Ramos

“…El 24 % percibe que hay violencia en los noviazgos, el 16 % no sabe y el 60 % dice que no. El conocimiento sobre los tres tipos de violencia es bajo, solo el 0,8 % reconoce los tres tipos de violencia; el 22,1% la física y psicológica; el 8,8 % solo la psicológica y el 1,3 % la física.…”
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Deep learning-based prediction of mortality using brain midline shift and clinical information by An-Rong Wu, Sun-Yuan Hsieh, Hsin-Hung Chou, Cheng-Shih Lai, Jo-Ying Hung, Bow Wang, Yi-Shan Tsai

“…The detected midlines were clearly separated into left and right brain with a dice coefficient of 0.98. The accuracy and AUC of the MLP model were both above 0.8. …”
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Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI by Sihwan Kim, Changmin Park, Gwanghyeon Jeon, Seohee Kim, Jong Hyo Kim

“…The on-demand correction performance of the algorithm was assessed using similarity metrics: volumetric Dice score, volume error percentage, average surface distance, and Hausdorff distance. …”
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MUNet: a novel framework for accurate brain tumor segmentation combining UNet and mamba networks by Lijuan Yang, Lijuan Yang, Qiumei Dong, Da Lin, Chunfang Tian, Xinliang Lü

“…Finally, we propose a new loss function that combines mIoU loss, Dice loss, and Boundary loss, which improves segmentation overlap, similarity, and boundary accuracy from multiple perspectives. …”
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PolyRes-Net: A Polyhierarchical Residual Network for Decoding Anatomical Complexity in Medical Image Segmentation by Amr Magdy, Khalid N. Ismail, Marghny H. Mohamed, Mahmoud Hassaballah, Haitham Mahmoud, Mohammed M. Abdelsamea

“…Four benchamar datasets are used for evaluating our model: Krusir-SEG, CVC ClinicDB, 2018 Data Science Bowl, and ISIC-2018 skin lesion segmentation challenge dataset based on two metrics: the Mean Dice Similarity Coefficient (mDSC) and the Mean Intersection Over Union (mIOU). …”
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Improving diagnostic precision in thyroid nodule segmentation from ultrasound images with a self-attention mechanism-based Swin U-Net model by Changan Yang, Muhammad Awais Ashraf, Mudassar Riaz, Pascal Umwanzavugaye, Kavimbi Chipusu, Kavimbi Chipusu, Hongyuan Huang, Yueqin Xu

“…Comparative evaluations were conducted against traditional models, including U-Net and DeepLabv3+.ResultsThe Swin U-Net model demonstrated superior performance, achieving an average Dice Similarity Coefficient (DSC) of 0.78, surpassing baseline models such as U-Net and DeepLabv3+. …”
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A novel approach to skin disease segmentation using a visual selective state spatial model with integrated spatial constraints by Yu Bai, Hai Zhou, Hongjie Zhu, Shimin Wen, Binbin Hu, Haotian Li, Huazhang Wang, Daji Ergu, Fangyao Liu

“…The accuracy of Mean Intersection Over Union, Dice Coefficient, Classification Accuracy and Sensitivity on ISIC2018 datasets reached 82.17, 90.21, 95.34 and 88.49, respectively, exceeding the best indicators of other models by 1.71, 0.27, 0.65 and 0.04, respectively. …”
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Radiomics Features Based on MRI-ADC Maps of Patients with Breast Cancer: Relationship with Lesion Size, Features Stability, and Model Accuracy by Begumhan BAYSAL, Hakan BAYSAL, Mehmet Bilgin ESER, Mahmut Bilal DOGAN, Orhan ALIMOGLU

“…The tumors were segmented by three observers based on diffusion-weighted imaging-registered ADC maps, and the volumetric agreement of these segmentations was evaluated using the Dice coefficient. Stability of radiomics features (n=851) was evaluated with intraclass correlation coefficient (ICC, &gt;0.75) and coefficient of variation (CoV, &lt;0.15). …”
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Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation by Ali Zifan, Katelyn Zhao, Madilyn Lee, Zihan Peng, Laura J. Roney, Sarayu Pai, Jake T. Weeks, Michael S. Middleton, Ahmed El Kaffas, Jeffrey B. Schwimmer, Claude B. Sirlin

“…<b>Results:</b> The model with a depth of 4 and filter sizes of [16, 64, 128, 256] achieved the highest mean adjusted Dice score of 0.921, outperforming the other configurations, using three-fold cross-validation with early stoppage. …”
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Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning by Wenjun Liao, Xiangde Luo, Lu Li, Jinfeng Xu, Yuan He, Hui Huang, Shichuan Zhang

“…Detection was evaluated via sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), while segmentation was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD95). …”
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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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