Your browser doesn't support javascript.
loading
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.
Moawad, Ahmed W; Janas, Anastasia; Baid, Ujjwal; Ramakrishnan, Divya; Jekel, Leon; Krantchev, Kiril; Moy, Harrison; Saluja, Rachit; Osenberg, Klara; Wilms, Klara; Kaur, Manpreet; Avesta, Arman; Pedersen, Gabriel Cassinelli; Maleki, Nazanin; Salimi, Mahdi; Merkaj, Sarah; von Reppert, Marc; Tillmans, Niklas; Lost, Jan; Bousabarah, Khaled; Holler, Wolfgang; Lin, MingDe; Westerhoff, Malte; Maresca, Ryan; Link, Katherine E; Tahon, Nourel Hoda; Marcus, Daniel; Sotiras, Aristeidis; LaMontagne, Pamela; Chakrabarty, Strajit; Teytelboym, Oleg; Youssef, Ayda; Nada, Ayaman; Velichko, Yuri S; Gennaro, Nicolo; Cramer, Justin; Johnson, Derek R; Kwan, Benjamin Y M; Petrovic, Boyan; Patro, Satya N; Wu, Lei; So, Tiffany; Thompson, Gerry; Kam, Anthony; Perez-Carrillo, Gloria Guzman; Lall, Neil; Albrecht, Jake; Anazodo, Udunna; Lingaru, Marius George; Menze, Bjoern H.
Afiliação
  • Moawad AW; Mercy Catholic Medical Center, Darby, PA.
  • Janas A; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Baid U; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Ramakrishnan D; Charité - Universitatsmedizin, Berlin, Germany.
  • Jekel L; Center for Biomedical Image Computing and Analytics, University of Pennsylvania School of Medicine, Philadelphia, PA.
  • Krantchev K; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Moy H; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Saluja R; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Osenberg K; University of Ulm, Ulm, Germany.
  • Wilms K; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Kaur M; DKFZ Division of Translational Neurooncology at the WTZ, German Cancer Consortium, DKTK Partner Site, University Hospital Essen, Essen, Germany.
  • Avesta A; German Cancer Research Center, Heidelberg, Germany.
  • Pedersen GC; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Maleki N; Charité - Universitatsmedizin, Berlin, Germany.
  • Salimi M; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Merkaj S; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • von Reppert M; Cornell University, Ithaca, NY.
  • Tillmans N; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Lost J; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Bousabarah K; University of Leipzig, Leipzig, Germany.
  • Holler W; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Lin M; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Westerhoff M; University of Leipzig, Leipzig, Germany.
  • Maresca R; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Link KE; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Tahon NH; Ludwig Maximillian University, Munich, Germany.
  • Marcus D; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Sotiras A; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • LaMontagne P; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Chakrabarty S; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Teytelboym O; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Youssef A; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Nada A; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Velichko YS; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Gennaro N; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Cramer J; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Johnson DR; University of Leipzig, Leipzig, Germany.
  • Kwan BYM; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Petrovic B; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Patro SN; University of Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
  • Wu L; Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • So T; ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT.
  • Thompson G; University of Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
  • Kam A; Visage Imaging, GmbH, Berlin, Germany.
  • Perez-Carrillo GG; Visage Imaging, GmbH, Berlin, Germany.
  • Lall N; Visage Imaging, Inc, San Diego, California, USA.
  • Albrecht J; Yale University School of Medicine, Department of Therapeutic Radiology, New Haven, CT.
  • Anazodo U; New York University School of Medicine, New York, NY.
  • Lingaru MG; University of Missouri, Columbia, MI.
  • Menze BH; Washington University, St. Louis, MI.
ArXiv ; 2023 Jun 01.
Article em En | MEDLINE | ID: mdl-37396600
ABSTRACT
Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: ArXiv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Panamá

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: ArXiv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Panamá