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

Abstract

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.

A CAD System for Lung Cancer Detection Using Hybrid Deep Learning Techniques

“…According to the World Health Organization (WHO) and [ 1 ], lung cancer has been reported as a widespread global disease that occurs due to the uncontrolled growth of tissues [ 1 , 2 ]. There has been a significant increase in the death rate from lung cancer [ 1 ].…”

“…Lungs are spongy organs in the body. They are responsible for intaking oxygen from inhalation and expending carbon dioxide through exhalation [ 1 , 2 , 3 ]. There are numerous signs and symptoms of lung cancer.…”

“…There are numerous signs and symptoms of lung cancer. These symptoms include, but are not limited to, persistent coughing, coughing blood, shortness of breath, wheezing, and pain in the bones and chest [ 1 , 2 ]. These symptoms typically appear when the disease is at an advanced stage as opposed to an early stage [ 1 , 3 , 4 , 5 , 6 , 7 ].…”

“…These symptoms typically appear when the disease is at an advanced stage as opposed to an early stage [ 1 , 3 , 4 , 5 , 6 , 7 ]. Several factors can lead to a patient’s increased risk of lung cancer, such as prolonged smoking, exposure to secondhand smoke [ 2 ], exposure to radiation, and a family history of lung cancer [ 1 , 3 , 6 , 7 ].…”

“…Sousa et al [ 2 ] analyzed a deep learning method developed on U-Net and ResNet34 structures. This procedure was performed on four different types of cross-cohort datasets.…”