Automatic captioning of early gastric cancer using magnification endoscopy with narrow-band imaging,Gastrointestinal Endoscopy

当前位置： X-MOL 学术 › Gastrointest. Endosc. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Automatic captioning of early gastric cancer using magnification endoscopy with narrow-band imaging
Gastrointestinal Endoscopy ( IF 7.7 ) Pub Date : 2022-07-30 , DOI: 10.1016/j.gie.2022.07.019
Lixin Gong ₁ , Min Wang ₂ , Lei Shu ₃ , Jie He ₄ , Bin Qin ₅ , Jiacheng Xu ₆ , Wei Su ₆ , Di Dong ₇ , Hao Hu ₈ , Jie Tian ₉ , Pinghong Zhou ₆

Affiliation

College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Department of Gastroenterology, Hepatology and Nutrition, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China.
Department of Gastroenterology, No. 1 Hospital of Wuhan, Wuhan, China.
Endoscopy Center, Zhongshan Hospital (Xiamen Branch), Fudan University, Xiamen, China; Department of Gastroenterology, The Affiliated Dongnan Hospital of Xiamen University, Zhangzhou, China.
Department of Gastroenterology, the Second Affiliated Hospital of Xi'an Jiaotong University, Zhangzhou, China.
Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China.
CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China; Department of Gastroenterology, Shigatse People's Hospital, Shigatse, China.
College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.

Background and Aims

The detection rate for early gastric cancer (EGC) is unsatisfactory, and mastering the diagnostic skills of magnifying endoscopy with narrow-band imaging (ME-NBI) requires rich expertise and experience. We aimed to develop an EGC captioning model (EGCCap) to automatically describe the visual characteristics of ME-NBI images for endoscopists.

Methods

ME-NBI images (n = 1886) from 294 cases were enrolled from multiple centers, and corresponding 5658 text data were designed following the simple EGC diagnostic algorithm. An EGCCap was developed using the multiscale meshed-memory transformer. We conducted comprehensive evaluations for EGCCap including the quantitative and quality of performance, generalization, robustness, interpretability, and assistant value analyses. The commonly used metrics were BLEUs, CIDEr, METEOR, ROUGE, SPICE, accuracy, sensitivity, and specificity. Two-sided statistical tests were conducted, and statistical significance was determined when P < .05.

Results

EGCCap acquired satisfying captioning performance by outputting correctly and coherently clinically meaningful sentences in the internal test cohort (BLEU1 = 52.434, CIDEr = 36.734, METEOR = 27.823, ROUGE = 49.949, SPICE = 35.548) and maintained over 80% performance when applied to other centers or corrupted data. The diagnostic ability of endoscopists improved with the assistance of EGCCap, which was especially significant (P < .05) for junior endoscopists. Endoscopists gave EGCCap an average remarkable score of 7.182, showing acceptance of EGCCap.

Conclusions

EGCCap exhibited promising captioning performance and was proven with satisfying generalization, robustness, and interpretability. Our study showed potential value in aiding and improving the diagnosis of EGC and facilitating the development of automated reporting in the future.

中文翻译：

使用带窄带成像的放大内窥镜自动描述早期胃癌

背景和目标

早期胃癌（EGC）的检出率并不理想，掌握窄带成像放大内镜（ME-NBI）的诊断技术需要丰富的专业知识和经验。我们的目标是开发一个 EGC 字幕模型 (EGCCap) 来自动为内窥镜医生描述 ME-NBI 图像的视觉特征。

方法

来自多个中心的 294 个病例的 ME-NBI 图像（n = 1886）被登记，并按照简单的 EGC 诊断算法设计了相应的 5658 个文本数据。EGCCap 是使用多尺度网状内存转换器开发的。我们对 EGCCap 进行了全面的评估，包括性能的定量和质量、泛化、稳健性、可解释性和辅助价值分析。常用的指标是 BLEU、CIDEr、METEOR、ROUGE、SPICE、准确性、灵敏度和特异性。进行了双侧统计检验，当P < .05 时确定统计显着性。

结果

EGCCap 通过在内部测试队列中输出正确且连贯的具有临床意义的句子（BLEU1 = 52.434，CIDEr = 36.734，METEOR = 27.823，ROUGE = 49.949，SPICE = 35.548）获得了令人满意的字幕性能，并在应用于其他中心时保持了 80% 以上的性能或损坏的数据。在 EGCCap 的帮助下，内窥镜医师的诊断能力得到提高，这对于初级内窥镜医师来说尤为重要 ( P < .05)。内窥镜医师给 EGCCap 的平均评分为 7.182，这表明 EGCCap 得到了认可。