Sharma Pallabi, Gautam Anmol, Maji Pallab, Pachori Ram Bilas, Balabantaray Bunil Kumar
IEEE Trans Biomed Eng. 2023 Apr;70(4):1330-1339. doi: 10.1109/TBME.2022.3216269. Epub 2023 Mar 21.
One of the fundamental and crucial tasks for the automated diagnosis of colorectal cancer is the segmentation of the acute gastrointestinal lesions, most commonly colorectal polyps. Therefore, in this work, we present a novel lightweight encoder-decoder mode of architecture with the attention mechanism to address this challenging task.
The proposed Li-SegPNet architecture harnesses cross-dimensional interaction in feature maps with novel encoder block with modified triplet attention. We have used atrous spatial pyramid pooling to handle the problem of segmenting objects at multiple scales. We also address the semantic gap between the encoder and decoder through a modified skip connection using attention gating.
We applied our model to colonoscopy still images and trained and validated it on two publicly available datasets, Kvasir-SEG and CVC-ClinicDB. We achieve mean Intersection-Over-Union (mIoU) and dice scores of 0.88, 0.9058 and 0.8969, 0.9372 on Kvasir-SEG and CVC-ClinicDB, respectively. We analyze the generalizability of Li-SegPNet by testing it on two independent previously unseen datasets, Hyper-Kvasir and EndoTect 2020, and establish the model efficiency in cross-dataset evaluation. We employ multi-scale testing to examine the model performance on different sizes of polyps. Li-SegPNet performs best on medium-sized polyps with a mIoU and dice score of 0.9086 and 0.9137, respectively on the Kvasir-SEG dataset and 0.9425, 0.9434 of mIoU and dice score, respectively on CVC-ClinicDB.
The experimental results convey that we establish a new benchmark on these four datasets for the segmentation of polyps.
The proposed model can be used as a new benchmark model for polyps segmentation. Lesser parameters in comparison to other models give the edge in the applicability of the proposed Li-SegPNet model in real-time clinical analysis.
结直肠癌自动诊断的一项基本且关键的任务是对急性胃肠道病变进行分割，最常见的是结直肠息肉。因此，在这项工作中，我们提出了一种具有注意力机制的新型轻量级编码器 - 解码器架构模式来解决这一具有挑战性的任务。
所提出的Li - SegPNet架构利用具有改进三元组注意力的新型编码器块在特征图中进行跨维度交互。我们使用空洞空间金字塔池化来处理多尺度分割对象的问题。我们还通过使用注意力门控的改进跳跃连接来解决编码器和解码器之间的语义差距。
我们将模型应用于结肠镜检查静止图像，并在两个公开可用的数据集Kvasir - SEG和CVC - ClinicDB上进行训练和验证。我们在Kvasir - SEG和CVC - ClinicDB上分别实现了平均交并比（mIoU）和骰子系数得分0.88、0.9058以及0.8969、0.9372。我们通过在两个独立的未见数据集Hyper - Kvasir和EndoTect 2020上进行测试来分析Li - SegPNet的泛化能力，并在跨数据集评估中确定模型效率。我们采用多尺度测试来检查模型在不同大小息肉上的性能。Li - SegPNet在中等大小息肉上表现最佳，在Kvasir - SEG数据集上mIoU和骰子系数得分分别为0.9086和0.9137，在CVC - ClinicDB上mIoU和骰子系数得分分别为0.9425和0.9434。
实验结果表明，我们在这四个数据集上为息肉分割建立了一个新的基准。
所提出的模型可作为息肉分割的新基准模型。与其他模型相比，参数较少使所提出的Li - SegPNet模型在实时临床分析中的适用性方面具有优势。
