مؤتمر
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification
| العنوان: | Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification |
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| المؤلفون: | Lambert, Benjamin, Roca, Pauline, Forbes, Florence, Doyle, Senan, Dojat, Michel |
| المساهمون: | Pixyl Medical Grenoble, GIN Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA) |
| المصدر: | MICCAI Workshop on 2nd Resource-Efficient Medical Image Analysis (REMIA) ; REMIA 2023 - 2nd Workshop on Resource-Efficient Medical Image Analysi ; https://hal.science/hal-04436251Test ; REMIA 2023 - 2nd Workshop on Resource-Efficient Medical Image Analysi, Oct 2023, Vancouver (BC), Canada. ⟨10.48550/arXiv.2308.11969⟩ |
| بيانات النشر: | HAL CCSD |
| سنة النشر: | 2023 |
| المجموعة: | Université Grenoble Alpes: HAL |
| مصطلحات موضوعية: | Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), Quantitative Methods (q-bio.QM), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Biological sciences, [STAT]Statistics [stat] |
| جغرافية الموضوع: | Vancouver (BC) |
| الوقت: | Vancouver (BC), Canada |
| الوصف: | International audience ; The burden of liver tumors is important, ranking as the fourth leading cause of cancer mortality. In case of hepatocellular carcinoma (HCC), the delineation of liver and tumor on contrast-enhanced magnetic resonance imaging (CE-MRI) is performed to guide the treatment strategy. As this task is time-consuming, needs high expertise and could be subject to inter-observer variability there is a strong need for automatic tools. However, challenges arise from the lack of available training data, as well as the high variability in terms of image resolution and MRI sequence. In this work we propose to compare two different pipelines based on anisotropic models to obtain the segmentation of the liver and tumors. The first pipeline corresponds to a baseline multi-class model that performs the simultaneous segmentation of the liver and tumor classes. In the second approach, we train two distinct binary models, one segmenting the liver only and the other the tumors. Our results show that both pipelines exhibit different strengths and weaknesses. Moreover we propose an uncertainty quantification strategy allowing the identification of potential false positive tumor lesions. Both solutions were submitted to the MICCAI 2023 Atlas challenge regarding liver and tumor segmentation. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/arxiv/2308.11969; hal-04436251; https://hal.science/hal-04436251Test; https://hal.science/hal-04436251/documentTest; https://hal.science/hal-04436251/file/remia.pdfTest; ARXIV: 2308.11969 |
| DOI: | 10.48550/arXiv.2308.11969 |
| الإتاحة: | https://doi.org/10.48550/arXiv.2308.11969Test https://hal.science/hal-04436251Test https://hal.science/hal-04436251/documentTest https://hal.science/hal-04436251/file/remia.pdfTest |
| حقوق: | http://creativecommons.org/licenses/byTest/ ; info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.E5307B89 |
| قاعدة البيانات: | BASE |
| DOI: | 10.48550/arXiv.2308.11969 |
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