Hi,
I understand that you are trying to calculate the precision, recall and IoU metrics on the deep learning predicted output and groundtruth. I also see from the .mat files attached that your volumetric groundtruth (volMask1) and predicted output (tempSeg1) are of the size 128x128x64.
The bboxPrecisionRecall() function currently supports only 2-D inputs for bboxes and groundTruthBboxes. Therefore, convert the 3-D volumes into 2-D images and you can refer to the following code that does this:
% Access 2-D images from 3-D volume and find the metric average
avgPrecision=0; totPrecision=0;
avgRecall=0;totRecall=0;
for i= 1:size(volmask1,3)
[precision,recall] = bboxPrecisionRecall(volMask1(:,:,i),tempSeg1(:,:,i));
totPrecision=totPrecision+precision
totRecall=totRecall+recall
end
avgPrecision = totalPrecision/size(volmask1,3);
avgRecall = totalRecall/size(volmask1,3);
Please refer to the following documentation for more details regarding precision recall computation on the data:
Thanks,
Anusha
