FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386045807> ?p ?o ?g. }

• W4386045807 abstract "The quality of segmentation of thyroid nodules in ultrasound images is a crucial factor in preventing the cancerization of thyroid nodules. However, the existing standards for the ultrasound imaging of cancerous nodules have limitations, and changes of the echo pattern of thyroid nodules pose challenges in accurately segmenting nodules, which can affect the diagnostic results of medical professionals.The aim of this study is to address the challenges related to segmentation accuracy due to noise, low contrast, morphological scale variations, and blurred edges of thyroid nodules in ultrasound images and improve the accuracy of ultrasound-based thyroid nodule segmentation, thereby aiding the clinical diagnosis of thyroid nodules.In this study, the dataset of thyroid ultrasound images was obtained from Hunan Provincial People's Hospital, consisting of a total of 3572 samples used for the training, validation, and testing of this model at a ratio of 8:1:1. A novel SK-Unet++ network was used to enhance the segmentation accuracy of thyroid nodules. SK-Unet++ is a novel deep learning architecture that adds the adaptive receptive fields based on the selective kernel (SK) attention mechanisms into the Unet++ network. The convolution blocks of the original UNet++ encoder part were replaced with finer SK convolution blocks in SK-Unet++. First, multiple skip connections were incorporated so that SK-Unet++ can make information from previous layers of the neural network to bypass certain layers and directly propagate to subsequent layers. The feature maps of the corresponding locations were fused on the channel, resulting in enhanced segmentation accuracy. Second, we added the adaptive receptive fields. The adaptive receptive fields were used to capture multiscale spatial features better by dynamically adjusting its receptive field. The assessment metrics contained dice similarity coefficient (Dsc), accuracy (Acc), precision (Pre), recall (Re), and Hausdorff distance, and all comparison experiments used the paired t-tests to assess whether statistically significant performance differences existed (p < 0.05). And to address the multi-comparison problem, we performed the false discovery rate (FDR) correction after the test.The segmentation model had an Acc of 80.6%, Dsc of 84.7%, Pre of 77.5%, Re of 71.7%, and an average Hausdorff distance of 15.80 mm. Ablation experimental results demonstrated that each module in the network could contribute to the improved performance (p < 0.05) and determined the best combination of parameters. A comparison with other state-of-the-art methods showed that SK-Unet++ significantly outperformed them in terms of segmentation performance (p < 0.05), with a more accurate segmentation contour. Additionally, the adaptive weight changes of the SK module were monitored during the training process, and the resulting change curves demonstrated their convergence.Our proposed method demonstrates favorable performance in the segmentation of ultrasound images of thyroid nodules. Results confirmed that SK-Unet++ is a feasible and effective method for the automatic segmentation of thyroid nodules in ultrasound images. The high accuracy achieved by our method can facilitate efficient screening of patients with thyroid nodules, ultimately reducing the workload of clinicians and radiologists." @default.
• W4386045807 created "2023-08-23" @default.
• W4386045807 creator A5029588463 @default.
• W4386045807 creator A5053636344 @default.
• W4386045807 creator A5060376792 @default.
• W4386045807 date "2023-08-22" @default.
• W4386045807 modified "2023-10-10" @default.
• W4386045807 title "SK‐Unet++: An improved Unet++ network with adaptive receptive fields for automatic segmentation of ultrasound thyroid nodule images" @default.
• W4386045807 cites W2107863647 @default.
• W4386045807 cites W2110065044 @default.
• W4386045807 cites W2114866336 @default.
• W4386045807 cites W2145150141 @default.
• W4386045807 cites W2147104572 @default.
• W4386045807 cites W2159207032 @default.
• W4386045807 cites W2294839422 @default.
• W4386045807 cites W2900728345 @default.
• W4386045807 cites W2922509574 @default.
• W4386045807 cites W3002393520 @default.
• W4386045807 cites W3004662472 @default.
• W4386045807 cites W3037439917 @default.
• W4386045807 cites W3084196418 @default.
• W4386045807 cites W3089914759 @default.
• W4386045807 cites W3092344722 @default.
• W4386045807 cites W3104610662 @default.
• W4386045807 cites W3129063931 @default.
• W4386045807 cites W3176031707 @default.
• W4386045807 cites W3176052592 @default.
• W4386045807 cites W4321232185 @default.
• W4386045807 cites W4321328679 @default.
• W4386045807 doi "https://doi.org/10.1002/mp.16672" @default.
• W4386045807 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37606374" @default.
• W4386045807 hasPublicationYear "2023" @default.
• W4386045807 type Work @default.
• W4386045807 citedByCount "0" @default.
• W4386045807 crossrefType "journal-article" @default.
• W4386045807 hasAuthorship W4386045807A5029588463 @default.
• W4386045807 hasAuthorship W4386045807A5053636344 @default.
• W4386045807 hasAuthorship W4386045807A5060376792 @default.
• W4386045807 hasConcept C108583219 @default.
• W4386045807 hasConcept C124504099 @default.
• W4386045807 hasConcept C126322002 @default.
• W4386045807 hasConcept C126838900 @default.
• W4386045807 hasConcept C138885662 @default.
• W4386045807 hasConcept C143753070 @default.
• W4386045807 hasConcept C151730666 @default.
• W4386045807 hasConcept C153180895 @default.
• W4386045807 hasConcept C154945302 @default.
• W4386045807 hasConcept C2776401178 @default.
• W4386045807 hasConcept C2776731575 @default.
• W4386045807 hasConcept C2779022025 @default.
• W4386045807 hasConcept C31972630 @default.
• W4386045807 hasConcept C41008148 @default.
• W4386045807 hasConcept C41895202 @default.
• W4386045807 hasConcept C526584372 @default.
• W4386045807 hasConcept C71924100 @default.
• W4386045807 hasConcept C81363708 @default.
• W4386045807 hasConcept C86803240 @default.
• W4386045807 hasConcept C89600930 @default.
• W4386045807 hasConceptScore W4386045807C108583219 @default.
• W4386045807 hasConceptScore W4386045807C124504099 @default.
• W4386045807 hasConceptScore W4386045807C126322002 @default.
• W4386045807 hasConceptScore W4386045807C126838900 @default.
• W4386045807 hasConceptScore W4386045807C138885662 @default.
• W4386045807 hasConceptScore W4386045807C143753070 @default.
• W4386045807 hasConceptScore W4386045807C151730666 @default.
• W4386045807 hasConceptScore W4386045807C153180895 @default.
• W4386045807 hasConceptScore W4386045807C154945302 @default.
• W4386045807 hasConceptScore W4386045807C2776401178 @default.
• W4386045807 hasConceptScore W4386045807C2776731575 @default.
• W4386045807 hasConceptScore W4386045807C2779022025 @default.
• W4386045807 hasConceptScore W4386045807C31972630 @default.
• W4386045807 hasConceptScore W4386045807C41008148 @default.
• W4386045807 hasConceptScore W4386045807C41895202 @default.
• W4386045807 hasConceptScore W4386045807C526584372 @default.
• W4386045807 hasConceptScore W4386045807C71924100 @default.
• W4386045807 hasConceptScore W4386045807C81363708 @default.
• W4386045807 hasConceptScore W4386045807C86803240 @default.
• W4386045807 hasConceptScore W4386045807C89600930 @default.
• W4386045807 hasLocation W43860458071 @default.
• W4386045807 hasLocation W43860458072 @default.
• W4386045807 hasOpenAccess W4386045807 @default.
• W4386045807 hasPrimaryLocation W43860458071 @default.
• W4386045807 hasRelatedWork W1977408463 @default.
• W4386045807 hasRelatedWork W2053849403 @default.
• W4386045807 hasRelatedWork W2498632914 @default.
• W4386045807 hasRelatedWork W2591669441 @default.
• W4386045807 hasRelatedWork W3029276719 @default.
• W4386045807 hasRelatedWork W3125611560 @default.
• W4386045807 hasRelatedWork W4226493464 @default.
• W4386045807 hasRelatedWork W4312417841 @default.
• W4386045807 hasRelatedWork W45411538 @default.
• W4386045807 hasRelatedWork W59410728 @default.
• W4386045807 isParatext "false" @default.
• W4386045807 isRetracted "false" @default.
• W4386045807 workType "article" @default.