Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2009.05752v1
• Date: Sat, 12 Sep 2020 08:54:54 GMT
• Title: Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks
• Authors: Faizan Munawar, Shoaib Azmat, Talha Iqbal, Christer Gr\"onlund, Hazrat Ali
• Abstract summary: This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given Chest X-ray (CXR) In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks.
• Score: 0.5249805590164902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3, and D4, respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results.

Related papers

• Generative AI: A Pix2pix-GAN-Based Machine Learning Approach for Robust and Efficient Lung Segmentation [0.7614628596146602]
  This study develops a deep learning framework using a Pix2pix Generative Adversarial Network (GAN) to segment pulmonary abnormalities from CXR images. The framework's image preprocessing and augmentation techniques were properly incorporated with a U-Net-inspired generator-discriminator architecture.
  arXiv  Detail & Related papers  (2024-12-14T13:12:09Z)
• Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images [45.29301790646322]
  Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning.
  arXiv  Detail & Related papers  (2024-07-02T19:30:25Z)
• A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection [41.454028276986946]
  We propose a novel framework Two-Stage Generative Model (TSGM) to improve brain tumor detection and segmentation. CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide.
  arXiv  Detail & Related papers  (2023-11-06T12:58:26Z)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images [49.24576562557866]
  We propose a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representations from X-ray images. RGMIM significantly improved performance in small data volumes, such as 5% and 10% of the training set compared to other methods.
  arXiv  Detail & Related papers  (2022-11-01T07:41:03Z)
• Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation [0.4419843514606336]
  We present a Multi-task Swin UNEt TRansformer network for simultaneous solving of two tasks in the CMRxMotion challenge: CMR segmentation and motion artifacts classification. We utilize both segmentation and classification as a multi-task learning approach which allows us to determine the diagnostic quality of CMR and generate masks at the same time.
  arXiv  Detail & Related papers  (2022-09-06T13:14:44Z)
• A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction [0.0]
  We propose using a method performed by radiologists called image segmentation and applying it to machine learning models to prove a better segmentation. Using Mask R-CNN, its ResNet backbone being pre-trained on the RSNA pneumonia detection challenge dataset, we can train a model on the Brats 2020 Brain Tumor dataset. We can see how well the method of image subtraction works by comparing it to models without image subtraction through DICE coefficient (F1 score), recall, and precision on the untouched test set.
  arXiv  Detail & Related papers  (2022-04-04T01:45:11Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Deep LF-Net: Semantic Lung Segmentation from Indian Chest Radiographs Including Severely Unhealthy Images [5.826056983051642]
  A chest radiograph, commonly called chest x-ray (CxR), plays a vital role in the diagnosis of various lung diseases. Precise lung segmentation is extremely challenging because of variance in the shape of the lung caused by health issues, age, and gender. The proposed work investigates the use of an efficient deep convolutional neural network for accurate segmentation of lungs from CxR.
  arXiv  Detail & Related papers  (2020-11-19T07:21:02Z)
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images [47.35184075381965]
  We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
  arXiv  Detail & Related papers  (2020-11-15T14:01:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.