Aims and objectives
Objectively evaluating progression and regression of lung pathologies on frontal Chest X-Ray (CXR) is a challenge.Universally,
the temporal evolution of most pathological conditions on chest x-rayis still subjective,
whichmight beacceptable for directional prediction butnotreliable for quantification of the change.
Here we present a novel Deep Learning (DL) based approach to automatically segment the chest cavity and lungs on a CXR for this purpose.
This method will be useful in the...
Methods and materials
Image dataset: The dataset involved 3531 XRAY images (1024x1024),
which were randomly separated into (2714) training,
and (475) test sets,
such that each patient had images in one set only.
The chest-cavity and lung masks were available for all cases. Model training: The XRAY images were resampled to 255x255 resolution and min-max normalization was applied to the (8-bit,
The model architecture is UNET.
(The contracting part of the network involves...
Sample Outputs: The chest cavity segmentation closely follows the left and right lung segmentation demonstrating repeatability in both the cases (Fig.
1 and Fig.
2).With the advancements in deep learning,
pneumothorax detection and segmentation algorithms are emerging within the market. The following experiment simulates what could be possible in the future with our lung segmentation algorithms paired with pneumothorax segmentation,
by using manually segmented PTX regions as a substitute for...
Automated segmentation of the Chest Cavity and Lucent lung cavities can help temporally track the extent of pathology on a CXR.
an LLC:CC ratio trend can act as a surrogate marker for a patient’s respiratory reserve in conditions like pleural effusion.
More research is required to establish this claim.
Laszlo Rusko GE Healhcare Hungary Bence utca 3, Vaci Greens Office,
Building C Budapest BU 1138 HU phone:+36 70 522 6853 e-mail: Laszlo.Rusko@ge.com Dr.
Vasantha Kumar Venugopal Center for Advanced Research in Imaging,
andGenomics, Mahajan Imaging New Delhi email : email@example.com
(2018) Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer.In: Hu Z.,
(eds) Advances in Computer Science for Engineering and Education.
Advances in Intelligent Systems and Computing,
Yanqing Zhang (2018)Towards Robust Lung...