- Irene Canavesi, B.Sc. student in Biomedical Engineering - Sara Caramaschi, B.Sc. student in Biomedical Engineering Lung cancer is one of the most frequently diagnosed cancer forms, with a mortality of 84.2% in 2018. Our project focuses on shortening diagnosis time and improving accuracy in the overall detection of this disease. We implemented a convolutional neural network capable of automatically identifying lungs on a CT image. Segmentation is a necessary first step for the development of an algorithm capable of identifying and classifying the tumor mass since errors in the ROI identification can lead to errors in the tumor mass recognition. The network architecture follows the structure of a preexisting network, the U-Net that performs well on medical images. We reached a very good test accuracy of 99.63%: the strength of our work lies in the large number of CT images of both healthy and sick patients, used for the training and validation of the network.

1. LunS
Lung Segmentation through
Artificial Neural Networks
virtual NGC - 22.07.20
Caramaschi Sara
Canavesi Irene
Marco D. Santambrogio
Eleonora D’Arnese

2. Lung Cancer
Mortality rate: 84.2 %
2.09 millions registered cases
1.76 millions deaths in 2018[1]
[1] Globocan. (2018). Lung Fact Sheet. International Agency for Research on Cancer, WHO, 876, 2018–2019. http://gco.iarc.fr/today
2Irene Canavesi, Sara Caramaschi LunS

3. Long diagnosis
time Automatization
of the process
3 LunSIrene Canavesi, Sara Caramaschi
Problem & applied solution
Screening exam
Biopsy

4. 4
Lung segmentation U-Net[2]
Diversified
dataset[3][4]
Irene Canavesi, Sara Caramaschi LunS
[2] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation.
https://doi.org/10.1007/978-3-319-24574-4_28
[3] https://cdas.cancer.gov/nlst/
[4] Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P. Lambin, P. (2019). Data From NSCLC-Radiomics [Data
set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9RE
Our idea

5. 5
Accuracy: 99.73 % Loss: 0.0073
Irene Canavesi, Sara Caramaschi LunS
Our results

6. 6Irene Canavesi, Sara Caramaschi LunS
Lung
segmentation
Tumor mass
identification
Conclusion & Future work

7. Thank you for your attention
Sara Caramaschi
sara1.caramaschi@mail.polimi.it
Eleonora D’Arnese
eleonora.darnese@polimi.it
Marco D. Santambrogio
marco.santambrogio@polimi.it
Irene Canavesi
irene.canavesi@mail.polimi.it