Harb Samir Farag, Ali Asem, Yousuf Mohamed, Elshazly Salwa, Farag Aly
Computer Vision and Image Processing Lab., UofL, Louisville, KY, 40292, USA.
Higher Technological Institute, 10th of Ramadan City, Egypt.
Int J Comput Assist Radiol Surg. 2025 Feb;20(2):279-287. doi: 10.1007/s11548-024-03319-4. Epub 2025 Jan 9.
This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings.
The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e., support). Segmentation starts by detecting the rectum using a Markov Random Field-based algorithm. Then, supervised sequential episodic training is applied to the remaining slices, while contrastive learning is employed to enhance feature discriminability, thereby improving segmentation accuracy.
The proposed method, evaluated on 98 abdominal scans of prepped patients, achieved a Dice coefficient of 97.3% and a polyp information preservation accuracy of 98.28%. Statistical analysis, including 95% confidence intervals, underscores the method's robustness and reliability. Clinically, this high level of accuracy is vital for ensuring the preservation of critical polyp details, which are essential for accurate automatic diagnostic evaluation. The proposed method performs reliably in scenarios with limited annotated data. This is demonstrated by achieving a Dice coefficient of 97.15% when the model was trained on a smaller number of annotated CT scans (e.g., 10 scans) than the testing dataset (e.g., 88 scans).
The proposed sequential segmentation approach achieves promising results in colon segmentation. A key strength of the method is its ability to generalize effectively, even with limited annotated datasets-a common challenge in medical imaging.
本文介绍了一种新颖的深度学习方法，即使在数据标注有限的情况下，也能大幅提高结肠分割的准确性，从而增强CT结肠成像流程在临床环境中的整体有效性。
所提出的方法通过引导式顺序情节训练整合3D上下文信息，其中通过利用其先前标记的CT切片（即支持切片）对查询CT切片进行分割。分割首先使用基于马尔可夫随机场的算法检测直肠。然后，对其余切片应用监督式顺序情节训练，同时采用对比学习来增强特征可辨别性，从而提高分割准确性。
在所准备患者的98次腹部扫描上评估所提出的方法，获得了97.3%的骰子系数和息肉信息保留准确率98.28%。包括95%置信区间在内的统计分析强调了该方法的稳健性和可靠性。在临床上，这种高水平的准确性对于确保关键息肉细节的保留至关重要这些细节对于准确的自动诊断评估必不可少。所提出的方法在注释数据有限的情况下表现可靠。当模型在比测试数据集（例如88次扫描）数量更少的注释CT扫描（例如10次扫描）上进行训练时，获得了97.15%的骰子系数，这证明了这一点。
所提出的顺序分割方法在结肠分割方面取得了有希望的结果。该方法的一个关键优势是即使在注释数据集有限的情况下也能有效泛化，这是医学成像中的一个常见挑战。
