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Abstract：
Objective - Addressing the challenges that come with identifying and delineating brain tumours in intraoperative ultrasound. Our goal is to both qualitatively and quantitatively assess the interobserver variation, amongst experienced neuro-oncological intraoperative ultrasound users (neurosurgeons and neuroradiologists), in detecting and segmenting brain tumours on ultrasound. We then propose that, due to the inherent challenges of this task, annotation by localisation of the entire tumour mass with a bounding box could serve as an ancillary solution to segmentation for clinical training, encompassing margin uncertainty and the curation of large datasets. Methods - 30 ultrasound images of brain lesions in 30 patients were annotated by 4 annotators - 1 neuroradiologist and 3 neurosurgeons. The annotation variation of the 3 neurosurgeons was first measured, and then the annotations of each neurosurgeon were individually compared to the neuroradiologist\'s, which served as a reference standard as their segmentations were further refined by cross-reference to the preoperative magnetic resonance imaging (MRI). The following statistical metrics were used: Intersection Over Union (IoU), Sørensen-Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). These annotations were then converted into bounding boxes for the same evaluation. Results - There was a moderate level of interobserver variance between the neurosurgeons [ I o U : 0.789 , D S C : 0.876 , H D : 103.227 ] and a larger level of variance when compared against the MRI-informed reference standard annotations by the neuroradiologist, mean across annotators [ I o U : 0.723 , D S C : 0.813 , H D : 115.675 ] . After converting the segments to bounding boxes, all metrics improve, most significantly, the interquartile range drops by [ I o U : 37 % , D S C : 41 % , H D : 54 % ] . Conclusion - This study highlights the current challenges with detecting and defining tumour boundaries in neuro-oncological intraoperative brain ultrasound. We then show that bounding box annotation could serve as a useful complementary approach for both clinical and technical reasons.
摘要：
目标-解决术中超声识别和描绘脑肿瘤的挑战。我们的目标是定性和定量评估观察者之间的变化，在经验丰富的神经肿瘤术中超声使用者（神经外科医生和神经放射科医生）中，在超声波上检测和分割脑肿瘤。然后我们建议，由于这项任务的内在挑战，通过用边界框定位整个肿瘤块的注释可以作为临床培训分割的辅助解决方案，包括边际不确定性和大型数据集的管理。方法对30例患者的30例脑病变的超声图像进行注释，由4位注释者-1位神经放射科医生和3位神经外科医生进行注释。首先测量了3名神经外科医生的注释变异，然后将每个神经外科医生的注释分别与神经放射学家进行比较，作为参考标准，因为通过交叉参考术前磁共振成像（MRI）进一步完善了它们的分割。使用了以下统计指标：联合交集(IoU)，Sørensen-Dice相似系数（DSC）和Hausdorff距离（HD）。然后将这些注释转换为边界框，以进行相同的评估。结果-神经外科医生之间的观察者间存在中等水平的差异[IoU：0.789，DSC:0.876,HD：103.227]和与神经放射学家的MRI参考标准注释相比，差异水平更大，注释者的平均值[IoU：0.723，DSC:0.813,HD：115.675]。将线段转换为边界框后，所有指标都有所改善，最重要的是，四分位数间距下降[IoU：37%，DSC：41%，HD：54%]。结论-本研究强调了在神经肿瘤术中脑超声中检测和定义肿瘤边界的当前挑战。然后，我们表明，出于临床和技术原因，边界框注释可以作为一种有用的补充方法。
