Chest X-ray (CXR) is one of the most commonly prescribed medical
imaging procedures. Such large volume of CXR scans place significant
workloads on radiologists and medical practitioners.
Organ segmentation is a crucial step to obtain effective computer-aided
detection on CXR.
Future applications include
Abnormal shape/size of lungs
cardiomegaly (enlargement of the heart), pneumothorax (lung collapse), pleural effusion, and emphysema
An initial step (preprocessing) for deeper analysis - eg. tumor detection
In this work, we demonstrate the effectiveness of Fully Convolution
Networks (FCN) to segment lung fields in CXR images.
FCN incorporates a critic network, consisting primarily of an encoder
and a decoder network to impose segmentation to CXR. During training,
the network learns to generate a mask which then can be used to segment
the organ. Via supervised learning, the FCN learns the higher order
structures and guides the segmentation model to achieve realistic
segmentation outcomes
Dataset
This architecture is proposed to segment out lungs from a chest
radiograph (colloquially know as chest X-Ray, CXR). The dataset is
known as the Montgomery County X-Ray Set,
which contains 138 posterior-anterior x-rays. The motivation being that
this information can be further used to detect chest abnormalities like
shrunken lungs or other structural deformities. This is especially
useful in detecting tuberculosis in patients.
Data Preprocessing
The x-rays are 4892x4020 pixels big. Due to GPU memory limitations, they are resized to 1024x1024(gcn) or 256x256(others)
The dataset is augmented by randomly rotating and flipping the images, and adding Gaussian noise to the images.
A few of the results of the various models have been displayed below. (Scores are mean scores)
| Model | Dice Score | IoU |
| ----- | ---------------|-----------|
|VGG UNet| 0.9623 | 0.9295 |
|SegNet | 0.9293 | 0.8731 |
|GCN | 0.907 | 0.8314 |
|HDC/DUC | 0.8501 | 0.7462 |