## 4 Conclusions  
This work presents a new dataset for polyp detection, localisation and segmentation. It provides 3433 polyp images together with a manually annotated binary mask of the polyp area. It also provides a set of clinical metadata for each of the lesions included. Besides, we have compared four different models trained with our PICCOLO dataset and two other publicly available datasets (CVC-EndoSceneStill and Kvasir-SEG). Our results show that the PICCOLO dataset is suitable for training deep learning models for polyp segmentation, which results in better generalization capabilities  
Figure 4: Jaccard index reported on the test set of the PICCOLO dataset for the four models. Each series represents the model trained with the corresponding dataset (**a**) U-Net + VGG16; (**b**) U-Net + Densenet121; (**c**) LinkNet + VGG16; (**d**) LinkNet + Densenet121.  
as well as better results for flat polyps. We also provide clinical metadata which, as far as the authors know, are not available in other publicly available datasets, and which might eventually increase the potential use of this dataset for polyp classification purposes.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/23/8501/s1](http://www.mdpi.com/2076-3417/10/23/8501/s1)]([http://www.mdpi.com/2076-3417/10/23/8501/s1](http://www.mdpi.com/2076-3417/10/23/8501/s1)), Table S1: Clinical metadata and distribution of lesions into training, validation and test sets. Table S2: Metrics obtained in the CVC-EndoSceneStill test set for the different models and datasets used during training. Table S3: Metrics obtained in the Kvasir-SEG test set for the different models and datasets used during training. Table S4: Metrics obtained in the PICCOLO test set for the different models and datasets used during training. Table S5: Metrics obtained for all test sets as a whole for the different models and datasets used during training. Figure S1: Uninformative frames.  
Conceptualization, L.F.S.-P., J.B.P. and A.P.; methodology, L.F.S.-P., J.B.P., A.P., N.A., R.B. and C.L.S.; software, L.F.S.-P., and A.P.; validation, L.F.S.-P., J.B.P. and A.P.; formal analysis, L.F.S.-P.; investigation, L.F.S.-P., A.J.C., F.P., N.A., R.B. and B.G.; resources, J.B.P., A.P., R.B., C.L.S., and F.M.S.-P.; data curation, L.F.S.-P., A.J.C., F.P., N.A. and B.G.; writing--original draft preparation, L.F.S.-P. and J.B.P.; writing--review and editing, L.F.S.-P., J.B.P., A.P., A.J.C., F.P., N.A., R.B., G., C.L.S., and F.M.S.-M.; visualization, L.F.S.-P.; supervision, J.B.P., A.P. and F.M.S.-M.; project administration, J.B.P., A.P. and C.L.S.; funding acquisition, J.B.P., A.P., R.B., C.L.S. and F.M.S.-M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by PICCOLO project. This project has received funding from the European Union's Horizon2020 research and innovation programme under grant agreement No 732111. The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein. Furthermore, this publication has also been partially supported by GR18199 from Consejeria de Economia, Ciencia y Agenda Digital of Junta de Extremadura (co-funded by European Regional Development Fund-ERDF. \"A way to make Europe\"/ \"Investing in your future\". This work has been performed by the ICTS \"NANIOISIS\" at the Jesus Uson Minimally Invasive Surgery Centre.  
Authors want to thank for their invaluable contribution to the data acquisition process to the pathologists from Hospital Universitatig Basurto: [PERSON], [PERSON] and [PERSON]; [PERSON] and technical staff from Basque Biobank, Basque Foundation for Health Innovation and Research-BIOEF; as well as [PERSON] and [PERSON].  
The authors declare no conflict of interest.