  The hippocampus is a crucial brain structure associated with various
psychiatric disorders, and its automatic and precise segmentation is essential
for studying these diseases. In recent years, deep learning-based methods have
made significant progress in hippocampus segmentation. However, training deep
neural network models requires substantial computational resources and time, as
well as a large amount of labeled training data, which is often difficult to
obtain in medical image segmentation. To address this issue, we propose a new
parameter-efficient fine-tuning method called LoRA-PT. This method transfers
the pre-trained UNETR model on the BraTS2021 dataset to the hippocampus
segmentation task. Specifically, the LoRA-PT method categorizes the parameter
matrix of the transformer structure into three sizes, forming three 3D tensors.
Through tensor singular value decomposition, these tensors are decomposed to
generate low-rank tensors with the principal singular values and singular
vectors, while the remaining singular values and vectors form the residual
tensor. During the fine-tuning, we only update the low-rank tensors, i.e. the
principal tensor singular values and vectors, while keeping the residual tensor
unchanged. We validated the proposed method on three public hippocampus
datasets. Experimental results show that LoRA-PT outperforms existing
parameter-efficient fine-tuning methods in segmentation accuracy while
significantly reducing the number of parameter updates. Our code is available
at https://github.com/WangangCheng/LoRA-PT/tree/LoRA-PT.
