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Communication Dans Un Congrès Année : 2021

Renal Cell Carcinoma Classification from Vascular Morphology

Rudan Xiao
  • Fonction : Auteur
Eric Debreuve
Damien Ambrosetti
Xavier Descombes

Résumé

Renal Cell Carcinoma (RCC) is one of the most common malignancies, and pathological diagnosis is the most reliable RCC diagnostic method. Recognizing the type of RCC tumor and the possibility of cell migration highly depends on the geometric and topological properties of the vascular network. Motivated by the diagnosis pipeline, we explore the use of the vascular network from the RCC histopathological image to boost the RCC classification result. To realize this, we firstly build a new vascular network-based RCC histopathological image dataset, namely VRCC200, with 200 well-labeled vascular network annotations. Based on these vascular networks of RCC histopathological images, we propose new hand-craft features, namely skeleton feature and lattice feature. These features well represent the geometric and topological properties of the vascular networks of RCC histopathological images. Then we build strong benchmark results with various algorithms (both traditional and deep learning models) on the VRCC200 dataset. The result of lattice features can beat the popular deep learning models with other features. Finally, we proved the robustness and advantage of our proposed features on a more patients' dataset VRCC60. All of the results of our experiments prove that the vascular network structure of RCC is one of the most important biomarkers for RCC diagnosis.
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Dates et versions

hal-03449971 , version 1 (26-11-2021)

Identifiants

Citer

Rudan Xiao, Eric Debreuve, Damien Ambrosetti, Xavier Descombes. Renal Cell Carcinoma Classification from Vascular Morphology. MICCAI 2021 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2021, Strasbourg, France. pp.611-621, ⟨10.1007/978-3-030-87231-1_59⟩. ⟨hal-03449971⟩
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