## 1 Introduction  
Medical image segmentation is an important task in clinical and research environments [1; 2; 3; 4], facilitating subsequent computational analyses, which depend on the accuracy of the segmentation [5; 6]. Manual expert annotations are currently considered the gold standard, which tend to be tedious, time-consuming, and often have limited reproducibility [3], even with the assistance of various tools [4].  
A plethora of fully automatic machine learning (ML) methods that can achieve state-of-the-art results have been proposed, but tend to face various challenges [7] that hinder clinical translation. Some of the most important challenges are generalization to unseen datasets and need for extensive expert corrections and refinements [4; 8]. Interactive machine learning (IML) methods fill the void between manual and automatic approaches by allowing an operator to train a patient-specific model via quick and rough drawings, which then automatically segments the entire scan [9; 10; 11]. IML approaches provide the option for expedited refinements, and the final segmentation tends to get closer to the desired result as a function of the invested time.  
Two popular tools offering IML functionality are ITK-SNAP [8] and 3D Slicer [12]. ITK-SNAP has seen success; however, it requires users to follow a complex protocol to achieve multi-label segmentation. The user first provides quick drawings for the different ROIs and trains a model. Afterwards and separately for each class, the user must place seeds and evolve a contour. 3D Slicer has tools for both interactive and automated methods using traditional techniques such as region-based statistical methods. Specifically regarding IML, 3D Slicer's \"grow from seeds\" effect works using GrowCut, but it can only support one image as input, putting at a disadvantage for segmentation of complex structures, like glioblastoma regions, which typically requires combination of information from multiple co-registered images, such as FLAIR, T1, T2, and T1 Gd. Deep learning can also be used for IML segmentation [13], but there has not been many successful methods in this category and it has only been demonstrated in simpler tasks. It is not simple to create and train methods that handle the diversity of biomedical image techniques, as well as the variable number of input channels.  
For manual segmentation, apart from the aforementioned tools, the Medical Imaging Interaction Toolkit (MITK) [14] v2021.02 has a complete set of segmentations utilities, with some allowing for interaction by means of seed placement, however they are target towards segmentation of homogenous lesions and use only one image as input. On the other end of the spectrum, the current state-of-the-art for fully automatic medical image segmentation is nnU-Net [15], a self-configuring U-Net-based method [16], which surpassed most specialized existing approaches in 23 public datasets used in international biomedical segmentation competitions.  
In this study, we propose an IML method leveraging adaptive geodesic distance (AGD) [17] maps alongside an ensemble of support vector machines (SVMs) that is agnostic to image type/dimensionality. We aimed to create a method that is easy-to-use, and supports multiparametric input images, in an effort to address obstacles that we identified were keeping interactive approaches from wider use, while also remaining fast and allowing the radiologist to control the decision-making. We systematically evaluated the performance of the proposed method against manual expert segmentation across different anatomical structures and image modalities. Evaluation endpoints comprised speed, spatial overlap agreement, and consistency between different time-points and raters.