  Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique
for the diagnosis and treatment of upper tract urothelial carcinoma (UTUC).
During ureteroscopy, the automatic segmentation of the hollow lumen is of
primary importance, since it indicates the path that the endoscope should
follow. In order to obtain an accurate segmentation of the hollow lumen, this
paper presents an automatic method based on Convolutional Neural Networks
(CNNs).
  Methods: The proposed method is based on an ensemble of 4 parallel CNNs to
simultaneously process single and multi-frame information. Of these, two
architectures are taken as core-models, namely U-Net based in residual
blocks($m_1$) and Mask-RCNN($m_2$), which are fed with single still-frames
$I(t)$. The other two models ($M_1$, $M_2$) are modifications of the former
ones consisting on the addition of a stage which makes use of 3D Convolutions
to process temporal information. $M_1$, $M_2$ are fed with triplets of frames
($I(t-1)$, $I(t)$, $I(t+1)$) to produce the segmentation for $I(t)$.
  Results: The proposed method was evaluated using a custom dataset of 11
videos (2,673 frames) which were collected and manually annotated from 6
patients. We obtain a Dice similarity coefficient of 0.80, outperforming
previous state-of-the-art methods.
  Conclusion: The obtained results show that spatial-temporal information can
be effectively exploited by the ensemble model to improve hollow lumen
segmentation in ureteroscopic images. The method is effective also in presence
of poor visibility, occasional bleeding, or specular reflections.
