A Novel Hybrid Parameter-Efficient Fine-Tuning Approach for Hippocampus Segmentation and Alzheimer's Disease Diagnosis

Deep learning methods have significantly advanced medical image segmentation, yet their success hinges on large volumes of manually annotated data, which require specialized expertise for accurate labeling. Additionally, these methods often demand substantial computational resources, particularly for three-dimensional medical imaging tasks. Consequently, applying deep learning techniques for medical image segmentation with limited annotated data and computational resources remains a critical challenge. In this paper, we propose a novel parameter-efficient fine-tuning strategy, termed HyPS, which employs a hybrid parallel and serial architecture. HyPS updates a minimal subset of model parameters, thereby retaining the pre-trained model's original knowledge tructure while enhancing its ability to learn specific features relevant to downstream tasks. We apply this strategy to the state-of-the-art SwinUNETR model for medical image segmentation. Initially, the model is pre-trained on the BraTs2021 dataset, after which the HyPS method is employed to transfer it to three distinct hippocampus datasets.Extensive experiments demonstrate that HyPS outperforms baseline methods, especially in scenarios with limited training samples. Furthermore, based on the segmentation results, we calculated the hippocampal volumes of subjects from the ADNI dataset and combined these with metadata to classify disease types. In distinguishing Alzheimer's disease (AD) from cognitively normal (CN) individuals, as well as early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI), HyPS achieved classification accuracies of 83.78% and 64.29%, respectively. These findings indicate that the HyPS method not only facilitates effective hippocampal segmentation using pre-trained models but also holds potential for aiding Alzheimer's disease detection. Our code is publicly available.

LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and Vectors

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. Similar to the LoRA method, during parameter 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 transfer learning methods in segmentation accuracy while significantly reducing the number of parameter updates.