  Volumetric lesion segmentation from computed tomography (CT) images is a
powerful means to precisely assess multiple time-point lesion/tumor changes.
However, because manual 3D segmentation is prohibitively time consuming,
current practices rely on an imprecise surrogate called response evaluation
criteria in solid tumors (RECIST). Despite their coarseness, RECIST markers are
commonly found in current hospital picture and archiving systems (PACS),
meaning they can provide a potentially powerful, yet extraordinarily
challenging, source of weak supervision for full 3D segmentation. Toward this
end, we introduce a convolutional neural network (CNN) based weakly supervised
slice-propagated segmentation (WSSS) method to 1) generate the initial lesion
segmentation on the axial RECIST-slice; 2) learn the data distribution on
RECIST-slices; 3) extrapolate to segment the whole lesion slice by slice to
finally obtain a volumetric segmentation. To validate the proposed method, we
first test its performance on a fully annotated lymph node dataset, where WSSS
performs comparably to its fully supervised counterparts. We then test on a
comprehensive lesion dataset with 32,735 RECIST marks, where we report a mean
Dice score of 92% on RECIST-marked slices and 76% on the entire 3D volumes.
