RRM-TransUNet: Deep-Learning Driven Interactive Model for Precise Pancreas Segmentation in CT Images.

BACKGROUND

Pancreatic diseases such as cancer and pancreatitis pose significant health risks. Early detection requires precise segmentation results. Fully automatic segmentation algorithms cannot integrate clinical expertise and correct output errors, while interactive methods can offer a better chance for higher accuracy and reliability.

METHODS

We proposed a new network-RRM-TransUNet for the interactive pancreas segmentation task in CT images aiming to provide more reliable and precise results. The network incorporates Rotary Position Embedding, Root Mean Square Normalisation, and a Mixture of Experts mechanism. An intuitive interface is constructed for user-aided pancreas segmentation.

RESULTS

RRM-TransUNet achieves outstanding performance on multiple datasets, with a Dice Similarity Coefficient (DSC) of 93.82% and an Average Symmetric Surface Distance error (ASSD) of 1.12 mm on MSD, 93.79%/1.15 mm on AMOS, and 93.68%/1.18 mm on AbdomenCT-1K.

CONCLUSION

Our method outperforms previous methods and provides doctors with an efficient and user-friendly interactive pancreas segmentation experience through the intuitive interface.