A multitask learning network with interactive fusion for surgical instrument segmentation

Abstract

The advent of surgical robots has enhanced the capabilities of minimally invasive surgery by providing surgeons with increased precision, dexterity, and control during operations. Accurate segmentation of surgical instruments in endoscopic images is critical to achieving these goals, as it allows surgical robots to precisely identify the instrument position and orientation, thereby reducing the risk of errors and ensuring safer and more successful procedures. However, the complexity of the surgical environment poses significant challenges to the accurate segmentation of surgical instruments, such as mirror reflections of surgical instruments, instrument occlusions, and motion disturbances. To address these issues, this paper presents an innovative multitask learning network with interactive fusion to increase the automatic segmentation accuracy and robustness of surgical instruments in endoscopic images during minimally invasive surgeries. Specifically, to effectively handle the diverse lighting conditions and dynamic environments encountered during surgeries, the proposed model leverages a combination of transformer and convolutional neural network (CNN) architectures to effectively extract both the global and local features of surgical instruments. Moreover, to enhance the boundary perception capability of surgical instruments within the context of endoscopic images, the proposed model incorporates an attention-guided multitask learning structure consisting of a main decoder focused on segmenting the instruments and an auxiliary edge decoder aimed at delineating the boundaries of the instruments. In addition, a dual attention enhancement (DAE) block is introduced, which employs attention mechanisms in different directions to enhance the network’s focus on key features while suppressing irrelevant features. Furthermore, given the diverse nature of surgical tools and their interactions within the surgical site, an atrous pyramid attention (APA) block is introduced to improve the network’s adaptability to the various shapes and sizes of surgical instruments. Experimental evaluations on two surgical instrument datasets demonstrate that the proposed model achieves superior segmentation performance, validating its effectiveness and highlighting its potential to advance the field of robotic-assisted minimally invasive surgery.