Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283024748> ?p ?o ?g. }
- W4283024748 endingPage "2099" @default.
- W4283024748 startingPage "2099" @default.
- W4283024748 abstract "Osteosarcoma is a malignant osteosarcoma that is extremely harmful to human health. Magnetic resonance imaging (MRI) technology is one of the commonly used methods for the imaging examination of osteosarcoma. Due to the large amount of osteosarcoma MRI image data and the complexity of detection, manual identification of osteosarcoma in MRI images is a time-consuming and labor-intensive task for doctors, and it is highly subjective, which can easily lead to missed and misdiagnosed problems. AI medical image-assisted diagnosis alleviates this problem. However, the brightness of MRI images and the multi-scale of osteosarcoma make existing studies still face great challenges in the identification of tumor boundaries. Based on this, this study proposed a prior guidance-based assisted segmentation method for MRI images of osteosarcoma, which is based on the few-shot technique for tumor segmentation and fine fitting. It not only solves the problem of multi-scale tumor localization, but also greatly improves the recognition accuracy of tumor boundaries. First, we preprocessed the MRI images using prior generation and normalization algorithms to reduce model performance degradation caused by irrelevant regions and high-level features. Then, we used a prior-guided feature abdominal muscle network to perform small-sample segmentation of tumors of different sizes based on features in the processed MRI images. Finally, using more than 80,000 MRI images from the Second Xiangya Hospital for experiments, the DOU value of the method proposed in this paper reached 0.945, which is at least 4.3% higher than other models in the experiment. We showed that our method specifically has higher prediction accuracy and lower resource consumption." @default.
- W4283024748 created "2022-06-18" @default.
- W4283024748 creator A5027630663 @default.
- W4283024748 creator A5045850921 @default.
- W4283024748 creator A5060470951 @default.
- W4283024748 creator A5065873184 @default.
- W4283024748 date "2022-06-16" @default.
- W4283024748 modified "2023-10-16" @default.
- W4283024748 title "Multi-Scale Tumor Localization Based on Priori Guidance-Based Segmentation Method for Osteosarcoma MRI Images" @default.
- W4283024748 cites W1901129140 @default.
- W4283024748 cites W1966477192 @default.
- W4283024748 cites W2556562139 @default.
- W4283024748 cites W2565639579 @default.
- W4283024748 cites W2591213449 @default.
- W4283024748 cites W2783895116 @default.
- W4283024748 cites W2799597343 @default.
- W4283024748 cites W2888570268 @default.
- W4283024748 cites W2892742603 @default.
- W4283024748 cites W2907868182 @default.
- W4283024748 cites W2924449894 @default.
- W4283024748 cites W2941032002 @default.
- W4283024748 cites W2972393260 @default.
- W4283024748 cites W3026598587 @default.
- W4283024748 cites W3027109052 @default.
- W4283024748 cites W3030790048 @default.
- W4283024748 cites W3042642124 @default.
- W4283024748 cites W3087050846 @default.
- W4283024748 cites W3087644100 @default.
- W4283024748 cites W3094268709 @default.
- W4283024748 cites W3096812112 @default.
- W4283024748 cites W3111997402 @default.
- W4283024748 cites W3113478493 @default.
- W4283024748 cites W3122471192 @default.
- W4283024748 cites W3124249089 @default.
- W4283024748 cites W3124429422 @default.
- W4283024748 cites W3127076201 @default.
- W4283024748 cites W3135283646 @default.
- W4283024748 cites W3163357200 @default.
- W4283024748 cites W3165898389 @default.
- W4283024748 cites W3170090128 @default.
- W4283024748 cites W3171757618 @default.
- W4283024748 cites W3195315042 @default.
- W4283024748 cites W3195376632 @default.
- W4283024748 cites W3196790784 @default.
- W4283024748 cites W3198789395 @default.
- W4283024748 cites W3203129667 @default.
- W4283024748 cites W3205247817 @default.
- W4283024748 cites W3215379938 @default.
- W4283024748 cites W3216730860 @default.
- W4283024748 cites W3217772895 @default.
- W4283024748 cites W4200087356 @default.
- W4283024748 cites W4200493171 @default.
- W4283024748 cites W4205525180 @default.
- W4283024748 cites W4206255522 @default.
- W4283024748 cites W4206273332 @default.
- W4283024748 cites W4206468162 @default.
- W4283024748 cites W4206558468 @default.
- W4283024748 cites W4210768746 @default.
- W4283024748 cites W4214811741 @default.
- W4283024748 cites W4220663381 @default.
- W4283024748 cites W4220945434 @default.
- W4283024748 cites W4224317283 @default.
- W4283024748 cites W4226393988 @default.
- W4283024748 cites W4229016463 @default.
- W4283024748 cites W4280563321 @default.
- W4283024748 cites W4280564359 @default.
- W4283024748 cites W4280578406 @default.
- W4283024748 cites W4280621297 @default.
- W4283024748 cites W4281954109 @default.
- W4283024748 doi "https://doi.org/10.3390/math10122099" @default.
- W4283024748 hasPublicationYear "2022" @default.
- W4283024748 type Work @default.
- W4283024748 citedByCount "15" @default.
- W4283024748 countsByYear W42830247482022 @default.
- W4283024748 countsByYear W42830247482023 @default.
- W4283024748 crossrefType "journal-article" @default.
- W4283024748 hasAuthorship W4283024748A5027630663 @default.
- W4283024748 hasAuthorship W4283024748A5045850921 @default.
- W4283024748 hasAuthorship W4283024748A5060470951 @default.
- W4283024748 hasAuthorship W4283024748A5065873184 @default.
- W4283024748 hasBestOaLocation W42830247481 @default.
- W4283024748 hasConcept C111472728 @default.
- W4283024748 hasConcept C115961682 @default.
- W4283024748 hasConcept C124504099 @default.
- W4283024748 hasConcept C126838900 @default.
- W4283024748 hasConcept C136886441 @default.
- W4283024748 hasConcept C138885662 @default.
- W4283024748 hasConcept C142724271 @default.
- W4283024748 hasConcept C143409427 @default.
- W4283024748 hasConcept C144024400 @default.
- W4283024748 hasConcept C153180895 @default.
- W4283024748 hasConcept C154945302 @default.
- W4283024748 hasConcept C19165224 @default.
- W4283024748 hasConcept C2776401178 @default.
- W4283024748 hasConcept C2777760704 @default.
- W4283024748 hasConcept C31972630 @default.
- W4283024748 hasConcept C41008148 @default.
- W4283024748 hasConcept C41895202 @default.