In medical imaging, efficient segmentation of colon polyps plays a pivotal role in minimally invasive solutions for colorectal cancer. This study introduces a novel approach employing two parallel encoder branches within a network for polyp segmentation. One branch of the encoder incorporates the dual convolution blocks that have the capability to maintain feature information over increased depths, and the other block embraces the single convolution block with the addition of the previous layer's feature, offering diversity in feature extraction within the encoder, combining them before transpose layers with a depth-wise concatenation operation. Our model demonstrated superior performance, surpassing several established deep-learning architectures on the Kvasir and CVC-ClinicDB datasets, achieved a Dice score of 0.919, a mIoU of 0.866 for the Kvasir dataset, and a Dice score of 0.931 and a mIoU of 0.891 for the CVC-ClinicDB. The visual and quantitative results highlight the efficacy of our model, potentially setting a new model in medical image segmentation.

翻译：在医学影像领域，结肠息肉的高效分割对于结直肠癌微创诊疗方案具有关键作用。本研究提出一种新颖的网络方法，采用双并行编码器分支进行息肉分割。编码器的一个分支采用能够保持深层特征信息的双卷积块，另一分支则采用融合前层特征的单卷积块，从而在编码器内部实现特征提取的多样性，并通过深度级联操作在转置层前进行特征融合。我们的模型在Kvasir和CVC-ClinicDB数据集上表现出卓越性能，超越了多种现有深度学习架构：在Kvasir数据集上获得Dice分数0.919、mIoU 0.866，在CVC-ClinicDB数据集上获得Dice分数0.931、mIoU 0.891。可视化与量化结果共同证明了本模型的有效性，有望为医学图像分割领域建立新的基准模型。