Multiorgan segmentation from partially labeled datasets with conditional nnU-Net

Zhang, Guobin; Yang, Zhiyong; Huo, Bin; Chai, Shude; Jiang, Shan

doi:10.1016/j.compbiomed.2021.104658

Cited by 21 publications

(7 citation statements)

References 45 publications

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“…Furthermore, nnU-Net was adopted as a training framework given its demonstrated success in other medical imaging tasks. 11,14,15 The trained model inputs a single iUS image and outputs a binary map of the tumor segmentation. All trainings were performed using full size images without pre-processing.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach

Natali,

Zhylka,

Olthof

et al. 2024

J. Med. Imag.

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Training and evaluation of the performance of a supervised deep-learning model for the segmentation of hepatic tumors from intraoperative US (iUS) images, with the purpose of improving the accuracy of tumor margin assessment during liver surgeries and the detection of lesions during colorectal surgeries.Approach: In this retrospective study, a U-Net network was trained with the nnU-Net framework in different configurations for the segmentation of CRLM from iUS. The model was trained on B-mode intraoperative hepatic US images, hand-labeled by an expert clinician. The model was tested on an independent set of similar images. The average age of the study population was 61.9 ± 9.9 years. Ground truth for the test set was provided by a radiologist, and three extra delineation sets were used for the computation of inter-observer variability.Results: The presented model achieved a DSC of 0.84 (p ¼ 0.0037), which is comparable to the expert human raters scores. The model segmented hypoechoic and mixed lesions more accurately (DSC of 0.89 and 0.88, respectively) than hyper-and isoechoic ones (DSC of 0.70 and 0.60, respectively) only missing isoechoic or >20 mm in diameter (8% of the tumors) lesions. The inclusion of extra margins of probable tumor tissue around the lesions in the training ground truth resulted in lower DSCs of 0.75 (p ¼ 0.0022). Conclusion:The model can accurately segment hepatic tumors from iUS images and has the potential to speed up the resection margin definition during surgeries and the detection of lesion in screenings by automating iUS assessment.

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Section: Deep Learning Modelsmentioning

confidence: 99%

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach

Natali,

Zhylka,

Olthof

et al. 2024

J. Med. Imag.

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“…And the combination may not be the most efficient. Reference 40 employed the state‐of‐the‐art nnU‐Net (no‐new‐U‐Net) 1 as the backbone and adopted a conditioning strategy by embedding auxiliary information into the decoder. The deep supervision mechanism was applied to refine the outputs at different scales.…”

Section: Related Workmentioning

confidence: 99%

Learning multi‐organ and tumor segmentation from partially labeled datasets by a conditional dynamic attention network

Li,

Lian,

Lin

et al. 2023

Concurrency and Computation

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SummaryMulti‐organ segmentation is a critical prerequisite for many clinical applications. Deep learning‐based approaches have recently achieved promising results on this task. However, they heavily rely on massive data with multi‐organ annotated, which is labor‐ and expert‐intensive and thus difficult to obtain. In contrast, single‐organ datasets are easier to acquire, and many well‐annotated ones are publicly available. It leads to the partially labeled issue: How to learn a unified multi‐organ segmentation model from several single‐organ datasets? Pseudo‐label‐based methods and conditional information‐based methods make up the majority of existing solutions, where the former largely depends on the accuracy of pseudo‐labels, and the latter has a limited capacity for task‐related features. In this paper, we propose the Conditional Dynamic Attention Network (CDANet). Our approach is designed with two key components: (1) multisource parameter generator, fusing the conditional and multiscale information to better distinguish among different tasks, and (2) dynamic attention module, promoting more attention to task‐related features. We have conducted extensive experiments on seven partially labeled challenging datasets. The results show that our method achieved competitive results compared with the advanced approaches, with an average Dice score of 75.08%. Additionally, the Hausdorff Distance is 26.31, which is a competitive result.

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“…Moreover, this study also did validation experiments on the pancreas dataset with some annotated data on the target domain. Zhang et al 38 propose a conditional nnU-Net to use the union of partially labeled datasets to perform multiorgan segmentation. This approach takes advantage of the richness of multiple organ images to compensate for the lack of labels through domain adaptation.…”

Section: F I G U R E 3 Visualization Of Different Intensity Intervals...mentioning

confidence: 99%

“…Moreover, this study also did validation experiments on the pancreas dataset with some annotated data on the target domain. Zhang et al 38 . propose a conditional nnU‐Net to use the union of partially labeled datasets to perform multiorgan segmentation.…”

Section: Introductionmentioning

confidence: 99%

Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning

Zhu

et al. 2022

Medical Physics

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Computer-aided automatic pancreas segmentation is essential for early diagnosis and treatment of pancreatic diseases. However, the annotation of pancreas images requires professional doctors and considerable expenditure. Due to imaging differences among various institution population, scanning devices, imaging protocols, and so on, significant degradation in the performance of model inference results is prone to occur when models trained with domain-specific (usually institution-specific) datasets are directly applied to new (other centers/institutions) domain data. In this paper, we propose a novel unsupervised domain adaptation method based on adversarial learning to address pancreas segmentation challenges with the lack of annotations and domain shift interference. Methods: A 3D semantic segmentation model with attention module and residual module is designed as the backbone pancreas segmentation model. In both segmentation model and domain adaptation discriminator network, a multiscale progressively weighted structure is introduced to acquire different field of views. Features of labeled data and unlabeled data are fed in pairs into the proposed multiscale discriminator to learn domain-specific characteristics. Then the unlabeled data features with pseudodomain label are fed to the discriminator to acquire domain-ambiguous information. With this adversarial learning strategy, the performance of the segmentation network is enhanced to segment unseen unlabeled data. Results: Experiments were conducted on two public annotated datasets as source datasets, respectively, and one private dataset as target dataset, where annotations were not used for the training process but only for evaluation. The 3D segmentation model achieves comparative performance with state-of -theart pancreas segmentation methods on source domain. After implementing our domain adaptation architecture, the average dice similarity coefficient (DSC) of the segmentation model trained on the NIH-TCIA source dataset increases from 58.79% to 72.73% on the local hospital dataset, while the performance of the target domain segmentation model transferred from the medical segmentation decathlon (MSD) source dataset rises from 62.34% to 71.17%. Conclusions: Correlations of features across data domains are utilized to train the pancreas segmentation model on unlabeled data domain, improving the generalization of the model. Our results demonstrate that the proposed method enables the segmentation model to make meaningful segmentation for unseen data of the training set. In the future, the proposed method has the potential to

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Multiorgan segmentation from partially labeled datasets with conditional nnU-Net

Cited by 21 publications

References 45 publications

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach

Learning multi‐organ and tumor segmentation from partially labeled datasets by a conditional dynamic attention network

Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning

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