## 4 Discussion and Conclusions  
In this study, we presented a general-purpose, easy-to-use, and fast IML-based segmentation method that can be applied in a multitude of research applications without requiring any adaptations to different domains or training of users. The method takes as input co-registered images and user drawings, to create AGD maps and train an ensemble of SVMs, used for segmenting the whole scan. We evaluated our method's performance on solid structures across different cohorts, image modalities, and anatomical sites. Our method utilizes the power of ML; however, it mitigates one of its known weaknesses, i.e., the need for extensive training and lack of reproducibility on new datasets. By virtue of being trained interactively, our segmentation models are optimal for the specific individual's scans. Additional benefits include the ability of the method to be parallelized and low hardware requirements. The disadvantage of this approach is that it is not fully automated. Our quantitative evaluation showed great promise for the applicability of the method in various structures relevant to medical research. Accuracy and inter-rater agreement were comparable to manual segmentation, while intra-rater agreement was high, indicating that the method is stable. Volumetric errors were mostly systematic, indicating that results can be improved through further iterations or volumetric operations like shrinking/expanding. Multiparametric image support allows the method to be used in more complex applications. The method was also shown to be fast and not require excessive interaction, which when combined with the low amount of training given to the clinical experts shows that the goal of creating an easy-to-use method was achieved. According to the evaluation of ITK-SNAP [11] on a subset of the BraTS dataset, in the glioblastoma regions, our study also evaluated, particularly, ET (Dice\({}_{\text{IML}}\)/Dice\({}_{\text{ITK-SNAP}}\) = 0.85/0.69) and WT (Dice\({}_{\text{IML}}\)/Dice\({}_{\text{ITK-SNAP}}\) = 0.94/0.85), and ITK-SNAP had a lower mean agreement with the ground truth. Time spent by users on inspecting images and drawing input annotations was also lower (Time\({}_{\text{IML}}\)/Time\({}_{\text{ITK-SNAP}}\) = 21 min/27.8 min), while our methodology was significantly less complex. Future research can improve this method on multiple fronts. Advanced ML techniques, such as semi-supervised learning, can potentially increase the accuracy and consistency of the results. Transfer learning could expand the range of tasks to non-solid structures, such as brain lesions. If a specific task is targeted, pre-trained population-derived models, atlases, and specialized preprocessing techniques can potentially aid in producing better segmentations. Furthermore, a prospective dataset, especially one acquired under different acquisition settings, would lend further validity to our method. The results showed that our method has accuracy and inter-rater consistency on par with manual segmentation across different solid anatomical structures and modalities. Additionally, our method showed high intra-rater consistency and minimized user interaction. Conceptualization, D.B., S.B., S.P., K.N. and C.D. ; methodology, D.B., S.B., S.P. and C.D. ; software, D.B., A.S. and S.P. ; validation, D.B., A.S., H.A., M.B., B.A.G., J.L., R.D.C., N.J., M.H., L.R. and S.I.K. ; formal analysis, D.B., S.B., S.R. and C.L. ; investigation, D.B., A.S., S.B., S.P. and S.R. ; resources, C.D. ; data curation, D.B., A.S., S.B., H.A., A.G., B.Y., A.L.S., R.K.G.D. and R.T.S. ; writing--original draft preparation, D.B., A.S., S.B., S.P., K.N. and C.D. ; writing--review and editing, D.B., A.S., S.B., S.P., S.R., H.A., K.N. and C.D. ; visualization, D.B. and A.S.; supervision, S.B., D.K., K.N. and C.D. ; project administration, S.B. and C.D. ; funding acquisition, S.B. and C.D. All authors have read and agreed to the published version of the manuscript.