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Abstract #4312

Leveraging transfer learning for post-operative brain tumor segmentation across MRI datasets

Catarina Passarinho1,2, Oscar Lally3, Ana Matoso1,4, Marta P. Loureiro1,2, José Maria Moreira4, Patrícia Figueiredo1, and Rita G. Nunes1
1Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal, 2Hospital da Luz, Luz Saúde, Lisbon, Portugal, 3King’s College London, London, United Kingdom, 4Hospital da Luz Learning Health, Lisbon, Portugal

Synopsis

Keywords: Segmentation, Segmentation

Motivation: Automated post-operative brain tumor segmentation is challenging due to treatment-related changes and limited availability of annotated datasets.

Goal(s): This work aims to assess whether transfer learning from models pre-trained on pre-operative data would improve post-operative tumor segmentation.

Approach: An ensemble segmentation model was trained on pre-operative data and then consecutively retrained on two post-operative datasets applying transfer learning to provide insights into model performance across pre- and post-operative contexts.

Results: Transfer learning significantly improved post-operative segmentation accuracy, particularly on related datasets, but performance declined when testing on data introduced earlier in training, highlighting the challenges of model knowledge retention.

Impact: This work addresses the hurdles of automated post-operative tumor segmentation by demonstrating that transfer learning from pre-operative models can improve post-treatment segmentation. The importance of large annotated datasets and the effects of catastrophic forgetting and model knowledge retention are highlighted.

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Keywords