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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RED-Net: A Neural Network for 3D Thyroid Segmentation in Chest CT Using Residual and Dilated Convolutions for Measuring Thyroid Volume by Min-Ji Kim, Jin-A Kim, Naae Kim, Yul Hwangbo, Hyun Jeong Jeon, Dong-Hwa Lee, Ji Eun Oh

“…The results showed that it achieved state-of-the-art performance with a Dice similarity coefficient of 0.8901.…”
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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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Advanced colon cancer detection: Integrating context-aware multi-image fusion (Camif) in a multi-stage framework by M.V.R. Vittal

“…It also demonstrated strong performance metrics with a specificity of 99.91, sensitivity of 99.10, accuracy of 98.18, and a Dice coefficient of 99.82, highlighting its robust capability in accurately detecting colon cancer.…”
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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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VP-SFDA: Visual Prompt Source-Free Domain Adaptation for Cross-Modal Medical Image by Yixin Chen, Yan Wang, Zhaoheng Xie

“…Notably, in the abdominal MRI to CT adaptation task, the VP-OS method achieved a remarkable improvement, increasing the average DICE score from 0.658 to 0.773 (P [Formula: see text] 0.01) and reducing the average surface distance (ASD) from 3.489 to 2.961 (P [Formula: see text] 0.01). …”
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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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El papel de la variación flexiva verbal en el procesamiento de las colocaciones léxicas verbo + nombre en español como lengua adicional by Mercedes Pérez Serrano, Matías Hidalgo Gallardo

“…Estos realizaron una tarea de decisión léxica a partir de ejemplos de veinte colocaciones verbo + nombre extraídas del Diccionario de Colocaciones del Español [DiCE] (Alonso Ramos 2004) y de diez combinaciones léxicas no documentadas en corpus. …”
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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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Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network by Qiyun Zhang, Jiawei Chen, Jinhong Wang, Haolin Wang, Yi He, Yi He, Bin Li, Zhemin Zhuang, Huancheng Zeng

“…Specifically, the performance metrics of the proposed model is as follows: IoU is 0.731, Dice is 0.817, Precision is 0.863, Recall is 0.803, and F1 score is 0.832. …”
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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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Intravoxel incoherent motion (IVIM)-derived perfusion fraction mapping for the visual evaluation of MR-guided high intensity focused ultrasound (MR-HIFU) ablation of uterine fibroi... by Derk J. Slotman, Lambertus W. Bartels, Ingrid M. Nijholt, Martijn Froeling, Judith A. F. Huirne, Chrit T.W Moonen, Martijn F. Boomsma

“…Contrast in perfusion fraction maps between areas with low perfusion fraction and surrounding tissue in the target uterine fibroid immediately following MR-HIFU treatment was evaluated. Additionally, the Dice similarity coefficient (DSC) was calculated between delineated areas with low IVIM-derived perfusion fraction and hypoperfusion based on CE-T1w.Results Average perfusion fraction ranged between 0.068 and 0.083 in areas with low perfusion fraction based on visual assessment, and between 0.256 and 0.335 in surrounding tissues (all p < 0.001). …”
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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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