## 1 Introduction  
Uterine fibroids represent the highest prevalence of benign tumors in women, with reports ranging anywhere from 4.5% to 68.6%, with a significant bias towards African American women [1]. It is estimated that the economic burden on the healthcare system from symptomatic women with uterine fibroids is nearly $34 billion [2]. For women with uterine fibroids, a major decision is determining whether fibroids can be successfully removed using minimally invasive (MI) techniques or their removal will require open surgery. Imaging variables that determine who is a candidate for MI surgery depends on the number of fibroids and locations of the myomas relative to the uterine wall. Interpreting these studies can be difficult as fibroids can lay on top of each other and present in any layer of the uterus. If a MI procedure is incorrectly selected, it can be significantly more difficult, if not impossible, thus increasing the risk of bleeding, and potentially aborting the MI procedure altogether, requiring a subsequent open surgery. It has been well documented that women in lower socio-economic status, particularly African American women, are less likely to be referred for minimally invasive procedures [3; 4; 5; 6] even though there is universal insurance coverage for them [7; 8]. The success of laparoscopic myometomies over the last few decades has significantly reduced the mortality of patients. In addition, it is associated with lower intensity of postoperative pain, shorter hospitalization, less blood loss, less postoperative complications, and faster recovery. Yet, the recurrence rate is higher than a classical myometomy, particularly in the case of small fibroids (e.g., <1 cm), and/or multiple myomas [9], which are more likely to be left in place after a laparoscopic myometomy [10; 11; 12]. In addition, the 5-year recurrence rate after laparoscopic myometomy reaches more than 50% [13]. Thus, tools to better remove all myomas to mitigate recurrence and to further improve procedural outcomes are desired. One of several options is to improve intra-procedural visualization, which currently the standard-of-care is the use of ultrasound. However, this imaging modality is often not used since it required a specialized technician and often provides difficult to interpret images. Currently the state-of-the-art visualization technology for fibroids (also known as myomas) is a pre-procedural MRI, which provides a holistic 3D view of the entire women's anatomy. This allows radiologists and gynecologists to more accurately determine the number and exact location of each myoma, relative to other anatomic structures (e.g., uterine wall, endometrium, cervix). However, the relatively poor spatial resolution MRI (on the order of millimeters) does still miss some small fibroids (<1 cm). This is not considered a significant disadvantage since the clinical burden of these small fibroids are not considered relevant and often would be left in to avoid other complications during the procedure. Although proven significantly beneficial for determining the best treatment option, the use of MRI for intra-procedural guidance is very limited. Currently, to the best of our knowledge, there are no known clinical myometomy studies on the use of MRI data intra-procedurally. However, it is known that currently gynecologists can leverage this pre-procedural MRI scan in two ways: (i) it can be directly viewed on a 2D monitor as 2D slices in a Digital Imaging Communication in Medicine (DICOM) Viewer, or (ii) it can be viewed from paper printouts that are hung next to physician during the procedure, shown in Figure 1. The goal for both methods is to convey the relative location of each fibroid to more easily allow the physician to find its location and determine where to make an incision to find it. The benefit of the first method is that it provides access to whole dataset where each slice can be toggled in real-time. However, this also requires someone to perform this manual toggling, which will take up procedural time to find each fibroid.