SIG-Former: monocular surgical instruction generation with transformers

• Published in: International journal for computer assisted radiology and surgery Vol. 17; no. 12; pp. 2203 - 2210
• Main Authors: Zhang, Jinglu, Nie, Yinyu, Chang, Jian, Zhang, Jian Jun
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2022; Springer Nature B.V
• Subjects: Cider; Color imagery; Computer Imaging; Computer Science; Datasets; Feature extraction; Feature maps; Health Informatics; Imaging; Medical imaging; Medicine; Medicine & Public Health; Original; Original Article; Pattern Recognition and Graphics; Radiology; Sentences; Surgery; Training; Transformers; Vision; Transformer; Image captioning; Surgical instruction generation; Reinforcement learning
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-022-02718-9

Purpose: Automatic surgical instruction generation is a crucial part for intra-operative surgical assistance. However, understanding and translating surgical activities into human-like sentences are particularly challenging due to the complexity of surgical environment and the modal gap between images and natural languages. To this end, we introduce SIG-Former , a transformer-backboned generation network to predict surgical instructions from monocular RGB images. Methods: Taking a surgical image as input, we first extract its visual attentive feature map with a fine-tuned ResNet-101 model, followed by transformer attention blocks to correspondingly model its visual representation, text embedding and visual–textual relational feature. To tackle the loss-metric inconsistency between training and inference in sequence generation, we additionally apply a self-critical reinforcement learning approach to directly optimize the CIDEr score after regular training. Results: We validate our proposed method on DAISI dataset, which contains 290 clinical procedures from diverse medical subjects. Extensive experiments demonstrate that our method outperforms the baselines and achieves promising performance on both quantitative and qualitative evaluations. Conclusion: Our experiments demonstrate that SIG-Former is capable of mapping dependencies between visual feature and textual information. Besides, surgical instruction generation is still at its preliminary stage. Future works include collecting large clinical dataset, annotating more reference instructions and preparing pre-trained models on medical images.