Joint Brain Tumor Segmentation from Multi‑magnetic Resonance Sequences through a Deep Convolutional Neural Network

Brain tumor segmentation is highly contributive in diagnosing and treatment planning. Manual brain tumor delineation is a time‑consuming and tedious task and varies depending on the radiologist’s skill. Automated brain tumor segmentation is of high importance and does not depend on either inter‑ or intra‑observation. The objective of this study is to automate the delineation of brain tumors from the Fluid-attenuated inversion recovery (FLAIR), T1‑weighted (T1W), T2‑weighted (T2W), and T1W contrast‑enhanced (T1ce) magnetic resonance (MR) sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein.

The BraTS‑2020 challenge dataset, containing 370 subjects with four MR sequences and manually delineated tumor masks, is applied to train a residual neural network. This network is trained and assessed separately for each one of the MR sequences (single‑channel input) and any combination thereof (dual‑ or multi‑channel input).

The quantitative assessment of the single‑channel models reveals that the FLAIR sequence would yield higher segmentation accuracy compared to its counterparts with a 0.77 ± 0.10 Dice index. As to considering the dual‑channel models, the model with FLAIR and T2W inputs yields a 0.80 ± 0.10 Dice index, exhibiting higher performance. The joint tumor segmentation on the entire four MR sequences yields the highest overall segmentation accuracy with a 0.82 ± 0.09 Dice index.

The FLAIR MR sequence is considered the best choice for tumor segmentation on a single MR sequence, while the joint segmentation on the entire four MR sequences would yield higher tumor delineation accuracy.