A Tumour and Liver Automatic Segmentation (ATLAS) Dataset on Contrast-Enhanced Magnetic Resonance Imaging for Hepatocellular Carcinoma

Quinton, Félix; Popoff, Romain; Presles, Benoît; Leclerc, Sarah; Mériaudeau, Fabrice; Nodari, Guillaume; Lopez, Olivier; Pellegrinelli, Julie; Chevallier, Olivier; Ginhac, Dominique; Vrigneaud, Jean-Marc; Alberini, Jean-Louis

doi:10.3390/data8050079

Cited by 15 publications

(2 citation statements)

References 17 publications

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“…Quinton et al. ( 67 ) published the first dataset of DCE-MRI in 2023, which is expected to facilitate the development of atlas-based automatic segmentation models for HCC GTVs.…”

Section: New Techniques For Determining Hcc Gtvsmentioning

confidence: 99%

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Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma

Meng,

Gong,

Liu

et al. 2024

Front. Oncol.

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Hepatocellular Carcinoma (HCC) is one of the most common malignant neoplasms. With the advancement of technology, the precision of radiotherapy (RT) for HCC has considerably increased, and it is an indispensable modality in the comprehensive management of HCC. Some RT techniques increase the radiation dose to HCC, which decreases the radiation dose delivered to the surrounding normal liver tissue. This approach significantly improves the efficacy of HCC treatment and reduces the incidence of Radiation-induced Liver Disease (RILD). Clear imaging and precise determination of the Gross Target Volume (GTV) are prerequisites of precise RT of HCC. The main hindrances in determining the HCC GTV include indistinct tumor boundaries on imaging and the impact on respiratory motion. The integration of multimodal imaging, four-dimensional imaging, and artificial intelligence (AI) techniques can help overcome challenges for HCC GTV. In this article, the advancements in medical imaging and precise determination for HCC GTV have been reviewed, providing a framework for the precise RT of HCC.

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“…Quinton et al. ( 67 ) published the first dataset of DCE-MRI in 2023, which is expected to facilitate the development of atlas-based automatic segmentation models for HCC GTVs.…”

Section: New Techniques For Determining Hcc Gtvsmentioning

confidence: 99%

“…Accurate segmentation of HCC tumors still faces significant challenges, one reason being the lack of standardized datasets (66). Quinton et al (67) published the first dataset of DCE-MRI in 2023, which is expected to facilitate the development of atlas-based automatic segmentation models for HCC GTVs.…”

Section: Atlas-based Image Segmentationmentioning

confidence: 99%

Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma

Meng,

Gong,

Liu

et al. 2024

Front. Oncol.

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Transformer‐ and joint learning‐based dual‐domain networks for undersampled MRI segmentation

Duan,

Huang,

Xie

et al. 2024

Medical Physics

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BackgroundRecently, magnetic resonance imaging (MRI) has become a crucial medical imaging technology widely used in clinical practice. However, MRI faces challenges such as the lengthy acquisition time for k‐space data and the need for time‐consuming manual annotation by radiologists. Traditionally, these challenges have been addressed individually through undersampled MRI reconstruction and automatic segmentation algorithms. Whether undersampled MRI segmentation can be enhanced by treating undersampled MRI reconstruction and segmentation as an end‐to‐end task, trained simultaneously, rather than as serial tasks should be explored.PurposeWe introduce a novel Transformer‐ and Joint Learning‐based Dual‐domain Network (TJLD‐Net) for undersampled MRI segmentation.MethodsThis method significantly enhances feature recognition in the segmentation process by fully utilizing the rich detail obtained during the image reconstruction phase. Consequently, the method can achieve precise and reliable image segmentation even with undersampled k‐space data. Additionally, it incorporates an attention mechanism for feature enhancement, which improves the representation of shared features by learning the contextual information in MR images.ResultsSimulation experiments demonstrate that the segmentation performance of TJLD‐Net on three datasets is significantly higher than that of the joint model (RecSeg) and six baseline models (where reconstruction and segmentation are regarded as serial tasks). On the CHAOS dataset, the Dice scores of TJLD‐Net are, on average, 9.87%, 2.17%, 1.90%, 1.80%, 9.60%, 0.80%, and 6.50% higher than those of the seven compared models. On the ATLAS challenge dataset, the average Dice scores of TJLD‐Net improve by 4.23%, 5.63%, 2.30%, 1.53%, 3.57%, 0.93%, and 6.60%. Similarly, on the SKM‐TEA dataset, the average Dice scores of TJLD‐Net improve by 4.73%, 12.80%, 14.83%, 8.67%, 4.53%, 11.60%, and 12.10%. The novel TJLD‐Net model provides a promising solution for undersampled MRI segmentation, overcoming the poor performance issues encountered by automated segmentation algorithms in low‐quality accelerated imaging.

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Anisotropic Hybrid Networks for Liver Tumor Segmentation with Uncertainty Quantification

Lambert,

Roca,

Forbes

et al. 2023

Lecture Notes in Computer Science

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A Tumour and Liver Automatic Segmentation (ATLAS) Dataset on Contrast-Enhanced Magnetic Resonance Imaging for Hepatocellular Carcinoma

Cited by 15 publications

References 17 publications

Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma

Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma

Transformer‐ and joint learning‐based dual‐domain networks for undersampled MRI segmentation

Anisotropic Hybrid Networks for Liver Tumor Segmentation with Uncertainty Quantification

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