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arXiv · 2406.07813

Evaluating the Impact of Sequence Combinations on Breast Tumor Segmentation in Multiparametric MRI

Hang Min·Gorane Santamaria Hormaechea·Prabhakar Ramachandran·Jason Dowling
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Abstract

Multiparametric magnetic resonance imaging (mpMRI) is a key tool for assessing breast cancer progression. Although deep learning has been applied to automate tumor segmentation in breast MRI, the effect of sequence combinations in mpMRI remains under-investigated. This study explores the impact of different combinations of T2-weighted (T2w), dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) map on breast tumor segmentation using nnU-Net. Evaluated on a multicenter mpMRI dataset, the nnU-Net model using DCE sequences achieved a Dice similarity coefficient (DSC) of 0.69 $\pm$ 0.18 for functional tumor volume (FTV) segmentation. For whole tumor mask (WTM) segmentation, adding the predicted FTV to DWI and ADC map improved the DSC from 0.57 $\pm$ 0.24 to 0.60 $\pm$ 0.21. Adding T2w did not yield significant improvement, which still requires further investigation under a more standardized imaging protocol. This study serves as a foundation for future work on predicting breast cancer treatment response using mpMRI.

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Hang Min, Gorane Santamaria Hormaechea, Prabhakar Ramachandran, Jason Dowling. 2024-06-12. Evaluating the Impact of Sequence Combinations on Breast Tumor Segmentation in Multiparametric MRI. https://arxiv.org/abs/2406.07813

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