  In clinical trials, one of the radiologists' routine work is to measure tumor
sizes on medical images using the RECIST criteria (Response Evaluation Criteria
In Solid Tumors). However, manual measurement is tedious and subject to
inter-observer variability. We propose a unified framework named SEENet for
semi-automatic lesion \textit{SE}gmentation and RECIST \textit{E}stimation on a
variety of lesions over the entire human body. The user is only required to
provide simple guidance by clicking once near the lesion. SEENet consists of
two main parts. The first one extracts the lesion of interest with the
one-click guidance, roughly segments the lesion, and estimates its RECIST
measurement. Based on the results of the first network, the second one refines
the lesion segmentation and RECIST estimation. SEENet achieves state-of-the-art
performance in lesion segmentation and RECIST estimation on the large-scale
public DeepLesion dataset. It offers a practical tool for radiologists to
generate reliable lesion measurements (i.e. segmentation mask and RECIST) with
minimal human effort and greatly reduced time.
