A method comprising: receiving, by a processor, an image comprising a cross-sectional view of an object; and for each of a plurality of models, generating, by the processor and using the respective model, a boundary prediction for the object within the image, wherein each boundary prediction comprises a plurality of boundary prediction portions and each model of the plurality of models comprises a unique model; the method characterized in that it further comprises: generating, by the processor, and based on the boundary prediction for each model of the plurality of models, a clustered boundary prediction for the object within the image, the clustered boundary prediction comprising a plurality of clustered boundary prediction portions that each correspond to one of the plurality of boundary prediction portions of each of the boundary predictions; for each of the plurality of clustered boundary prediction portions, determining, by the processor, and based on a comparison between the respective clustered boundary prediction portion and the corresponding boundary prediction portions of the boundary predictions, a confidence level for the respective clustered boundary prediction portion; and outputting, by the processor and for display on a display device, a graphical indication for each of the plurality of clustered boundary prediction portions, wherein each clustered boundary prediction portion has a visual characteristic indicative of the confidence level for the respective clustered boundary prediction portion. receiving, by a processor, an image comprising a cross-sectional view of an object; and for each of a plurality of models, generating, by the processor and using the respective model, a boundary prediction for the object within the image, wherein each boundary prediction comprises a plurality of boundary prediction portions and each model of the plurality of models comprises a unique model; the method characterized in that it further comprises: generating, by the processor, and based on the boundary prediction for each model of the plurality of models, a clustered boundary prediction for the object within the image, the clustered boundary prediction comprising a plurality of clustered boundary prediction portions that each correspond to one of the plurality of boundary prediction portions of each of the boundary predictions; for each of the plurality of clustered boundary prediction portions, determining, by the processor, and based on a comparison between the respective clustered boundary prediction portion and the corresponding boundary prediction portions of the boundary predictions, a confidence level for the respective clustered boundary prediction portion; and outputting, by the processor and for display on a display device, a graphical indication for each of the plurality of clustered boundary prediction portions, wherein each clustered boundary prediction portion has a visual characteristic indicative of the confidence level for the respective clustered boundary prediction portion. generating, by the processor, and based on the boundary prediction for each model of the plurality of models, a clustered boundary prediction for the object within the image, the clustered boundary prediction comprising a plurality of clustered boundary prediction portions that each correspond to one of the plurality of boundary prediction portions of each of the boundary predictions; for each of the plurality of clustered boundary prediction portions, determining, by the processor, and based on a comparison between the respective clustered boundary prediction portion and the corresponding boundary prediction portions of the boundary predictions, a confidence level for the respective clustered boundary prediction portion; and outputting, by the processor and for display on a display device, a graphical indication for each of the plurality of clustered boundary prediction portions, wherein each clustered boundary prediction portion has a visual characteristic indicative of the confidence level for the respective clustered boundary prediction portion.