Segmenting Medical Images with Limited Data

While computer vision has proven valuable for medical image segmentation, its application faces challenges such as limited dataset sizes and the complexity of effectively leveraging unlabeled images. To address these challenges, we present a novel semi-supervised, consistency-based approach termed the data-efficient medical segmenter (DEMS). The DEMS features an encoder-decoder architecture and incorporates the developed online automatic augmenter (OAA) and residual robustness enhancement (RRE) blocks. The OAA augments input data with various image transformations, thereby diversifying the dataset to improve the generalization ability. The RRE enriches feature diversity and introduces perturbations to create varied inputs for different decoders, thereby providing enhanced variability. Moreover, we introduce a sensitive loss to further enhance consistency across different decoders and stabilize the training process. Extensive experimental results on both our own and three public datasets affirm the effectiveness of DEMS. Under extreme data shortage scenarios, our DEMS achieves 16.85\% and 10.37\% improvement in dice score compared with the U-Net and top-performed state-of-the-art method, respectively. Given its superior data efficiency, DEMS could present significant advancements in medical segmentation under small data regimes. The project homepage can be accessed at https://github.com/NUS-Tim/DEMS.

MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis

Data Augmentation (DA) technique has been widely implemented in the computer vision field to relieve the data shortage, while the DA in Medical Image Analysis (MIA) is still mostly experience-driven. Here, we develop a plug-and-use DA method, named MedAugment, to introduce the automatic DA argumentation to the MIA field. To settle the difference between natural images and medical images, we divide the augmentation space into pixel augmentation space and spatial augmentation space. A novel operation sampling strategy is also proposed when sampling DA operations from the spaces. To demonstrate the performance and universality of MedAugment, we implement extensive experiments on four classification datasets and three segmentation datasets. The results show that our MedAugment outperforms most state-of-the-art DA methods. This work shows that the plug-and-use MedAugment may benefit the MIA community. Code is available at https://github.com/NUS-Tim/MedAugment_Pytorch.