Robotic surgery offers enhanced precision and adaptability, paving the way for automation in surgical interventions. Cholecystectomy, the gallbladder removal, is particularly well-suited for automation due to its standardized procedural steps and distinct anatomical boundaries. A key challenge in automating this procedure is dissecting with accuracy and adaptability. This paper presents a vision-based autonomous robotic dissection architecture that integrates real-time segmentation, keypoint detection, grasping and stretching the gallbladder with the left arm, and dissecting with the other arm. We introduce an improved segmentation dataset based on videos of robotic cholecystectomy performed by various surgeons, incorporating a new ``liver bed'' class to enhance boundary tracking after multiple rounds of dissection. Our system employs state-of-the-art segmentation models and an adaptive boundary extraction method that maintains accuracy despite tissue deformations and visual variations. Moreover, we implemented an automated grasping and pulling strategy to optimize tissue tension before dissection upon our previous work. Ex vivo evaluations on porcine livers demonstrate that our framework significantly improves dissection precision and consistency, marking a step toward fully autonomous robotic cholecystectomy.