Multimodal fusion promises better pancreas segmentation. However, where to perform fusion in models is still an open question. It is unclear if there is a best location to fuse information when analyzing pairs of imperfectly aligned images. Two main alignment challenges in this pancreas segmentation study are 1) the pancreas is deformable and 2) breathing deforms the abdomen. Even after image registration, relevant deformations are often not corrected. We examine how early through late fusion impacts pancreas segmentation. We used 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas labels. We used image registration (deeds) to align the image pairs. We trained a collection of basic UNets with different fusion points, spanning from early to late, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images. We assessed generalization of fusion points on nnUNet. The single-modality T2w baseline using a basic UNet model had a Dice score of 0.73, while the same baseline on the nnUNet model achieved 0.80. For the basic UNet, the best fusion approach occurred in the middle of the encoder (early/mid fusion), which led to a statistically significant improvement of 0.0125 on Dice score compared to the baseline. For the nnUNet, the best fusion approach was na\"ive image concatenation before the model (early fusion), which resulted in a statistically significant Dice score increase of 0.0021 compared to baseline. Fusion in specific blocks can improve performance, but the best blocks for fusion are model specific, and the gains are small. In imperfectly registered datasets, fusion is a nuanced problem, with the art of design remaining vital for uncovering potential insights. Future innovation is needed to better address fusion in cases of imperfect alignment of abdominal image pairs.
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