Automated segmentation of large volumes of medical images is often plagued by
the limited availability of fully annotated data and the diversity of organ
surface properties resulting from the use of different acquisition protocols
for different patients. In this paper, we introduce a more promising few-shot
learning-based method named Region-enhanced Prototypical Transformer (RPT) to
mitigate the effects of large intra-class diversity/bias. First, a subdivision
strategy is introduced to produce a collection of regional prototypes from the
foreground of the support prototype. Second, a self-selection mechanism is
proposed to incorporate into the Bias-alleviated Transformer (BaT) block to
suppress or remove interferences present in the query prototype and regional
support prototypes. By stacking BaT blocks, the proposed RPT can iteratively
optimize the generated regional prototypes and finally produce rectified and
more accurate global prototypes for Few-Shot Medical Image Segmentation (FSMS).
Extensive experiments are conducted on three publicly available medical image
datasets, and the obtained results show consistent improvements compared to
state-of-the-art FSMS methods. The source code is available at:
this https URL

通过区域原型和抑制干扰的转换器可以提高少样本医学图像分割的准确性和性能。