Objective: Bleeding impairs observation during neurosurgery, and excessive bleeding endangers the life of a patient. Thus, hemostasis is important during neurosurgery. The detection of bleeding areas is a prerequisite for hemostasis. Methods: To the best of our knowledge, this paper is the first to present results on the detection of neurosurgical craniotomy bleeding scenarios, i.e., scalp incision bleeding, skull incision bleeding, and dura matter-incision bleeding. This is realized via a workflow that combines craniotomy image data preparation and a Mask R-CNN framework. Bleeding images on a porcine skin tissue with a simulated blood injected by a syringe are taken by a visible light camera, and the video frames of the scalp incision, skull incision, and dura matter-incision bleeding are extracted from neurosurgical videos. Results: The precision of bleeding areas detection for the simulated bleeding scenario and the three craniotomy phase scenarios were 94.40%, 84.44%, 89.48%, and 90.46%. Conclusion: The contours of the neurosurgical craniotomy bleeding regions can be detected along with the bleeding areas. Significance: It is beneficial for neurosurgeons to identify the bleeding areas by sending prioritized alerts for bleeding events. Furthermore, it is valuable for a task-level medical robot designed for a neurosurgical pro-cedure, such as craniotomy, or a high-level robot designed for an entire neurosurgery procedure.