  We propose a fully-automated method for accurate and robust detection and
segmentation of potentially cancerous lesions found in the liver and in lymph
nodes. The process is performed in three steps, including organ detection,
lesion detection and lesion segmentation. Our method applies machine learning
techniques such as marginal space learning and convolutional neural networks,
as well as active contour models. The method proves to be robust in its
handling of extremely high lesion diversity. We tested our method on volumetric
computed tomography (CT) images, including 42 volumes containing liver lesions
and 86 volumes containing 595 pathological lymph nodes. Preliminary results
under 10-fold cross validation show that for both the liver lesions and the
lymph nodes, a total detection sensitivity of 0.53 and average Dice score of
$0.71 \pm 0.15$ for segmentation were obtained.
