Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Sousa, J (Author) Other; Pereira, T (Author) Other; Silva, F (Author) Other; Silva, MC (Author) Other; Vilares, AT (Author) Other; Cunha, A (Author) Other; Oliveira, HP (Author) FCUP

Journal

Title: Applied Sciences

Vol. 10

Final page: 1959

Publisher: MDPI

Indexing

ISI Web of Knowledge - 19 Citations

Scopus - 21 Citations

Other information

Authenticus ID: P-00W-340

DOI: 10.3390/app12041959

Abstract (EN): Lung cancer is one of the most common causes of cancer-related mortality, and since the majority of cases are diagnosed when the tumor is in an advanced stage, the 5-year survival rate is dismally low. Nevertheless, the chances of survival can increase if the tumor is identified early on, which can be achieved through screening with computed tomography (CT). The clinical evaluation of CT images is a very time-consuming task and computed-aided diagnosis systems can help reduce this burden. The segmentation of the lungs is usually the first step taken in image analysis automatic models of the thorax. However, this task is very challenging since the lungs present high variability in shape and size. Moreover, the co-occurrence of other respiratory comorbidities alongside lung cancer is frequent, and each pathology can present its own scope of CT imaging appearances. This work investigated the development of a deep learning model, whose architecture consists of the combination of two structures, a U-Net and a ResNet34. The proposed model was designed on a cross-cohort dataset and it achieved a mean dice similarity coefficient (DSC) higher than 0.93 for the 4 different cohorts tested. The segmentation masks were qualitatively evaluated by two experienced radiologists to identify the main limitations of the developed model, despite the good overall performance obtained. The performance per pathology was assessed, and the results confirmed a small degradation for consolidation and pneumocystis pneumonia cases, with a DSC of 0.9015 +/- 0.2140 and 0.8750 +/- 0.1290, respectively. This work represents a relevant assessment of the lung segmentation model, taking into consideration the pathological cases that can be found in the clinical routine, since a global assessment could not detail the fragilities of the model.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16