Titlebook: Kidney and Kidney Tumor Segmentation; MICCAI 2021 Challeng Nicholas Heller,Fabian Isensee,Christopher Weight Conference proceedings 2022 Sp

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書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation影響因子(影響力)




書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation被引頻次




書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation被引頻次學(xué)科排名




書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation年度引用




書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation年度引用學(xué)科排名




書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation讀者反饋




書(shū)目名稱(chēng)Kidney and Kidney Tumor Segmentation讀者反饋學(xué)科排名

Exonerate

Allure

,Extraction of?Kidney Anatomy Based on?a?3D U-ResNet with?Overlap-Tile Strategy,, while a rule-based postprocessing was applied to remove false-positive artefacts. Our model achieved 0.812 average dice, 0.694 average surface dice and 0.7 tumor dice. This led to the 12.5th position in the KiTS21 challenge.

惰性女人

,An Ensemble of?3D U-Net Based Models for?Segmentation of?Kidney and?Masses in?CT Scans,tions, including the use of transfer learning, an unsupervised regularized loss, custom postprocessing, and multi-annotator ground truth that mimics the evaluation protocol. Our submission has reached the 2nd place in the KiTS21 challenge.

fatuity

,Automated Kidney Tumor Segmentation with?Convolution and?Transformer Network,r improve segmentation performance. Experimental results on the 2021 kidney and kidney tumor segmentation (kits21) challenge demonstrated that our method achieved average dice of 61.6%, surface dice of 49.1%, and tumor dice of 50.52%, respectively, which ranked the . place on the kits21 challenge.

conscience

,A Two-Stage Cascaded Deep Neural Network with?Multi-decoding Paths for?Kidney Tumor Segmentation,. We evaluated our method on the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) dataset. The method achieved Dice score, Surface Dice and Tumor Dice of 69.4%, 56.9% and 51.9% respectively, in the test cases. The model of cascade network proposed in this paper has a promising application prospect in kidney cancer diagnosis.

Aqueous-Humor

2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst,work ResSENormUnet; then, the kidney and the tumor and cyst are fine-segmented by the second stage network DenseTransUnet, and finally, a post-processing operation based on a 3D connected region is used for the removal of false-positive segmentation results. We evaluate this approach in the KiTS21 challenge, which shows promising performance.

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,A Coarse-to-Fine Framework for?the?2021 Kidney and?Kidney Tumor Segmentation Challenge,ion of the kidney and kidney mass. The average Dice and surface Dice of segmentation predicted by our method on the test are 0.9077 and 0.8262, respectively. Our method outperformed all other teams and achieved . in the KITS2021 challenge.