LF-SegNet: A Fully Convolutional Encoder–Decoder Network for Segmenting Lung Fields from Chest Radiographs

Mittal, Ajay; Hooda, Rahul; Sofat, Sanjeev

doi:10.1007/s11277-018-5702-9

Cited by 65 publications

(40 citation statements)

References 31 publications

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“…Table 2 shows that for all four datasets the APPAU-Net model with the new KLTV loss consistently outperformed the APPAU-Net model with TV and XETV losses in both overlap and distance measures, and suggests that the model with KLTV loss generalizes better in multi-task learning. While both TV and XETV losses tend to lose some accuracy because of the additional classification task, KLTV still achieves good accuracy, comparable to fully-supervised segmentation models in Table 1 and LF-segnet [2]. Figure 3 shows the segmented lungs by different models, confirming the superior performance of our APPAU-Net with KLTV loss compared to the TV loss.…”

Section: Experiments and Resultssupporting

confidence: 52%

Semi-supervised Multi-task Learning with Chest X-Ray Images

Imran

Terzopoulos

2019

Machine Learning in Medical Imaging

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Discriminative models that require full supervision are inefficacious in the medical imaging domain when large labeled datasets are unavailable. By contrast, generative modeling-i.e., learning data generation and classification-facilitates semi-supervised training with limited labeled data. Moreover, generative modeling can be advantageous in accomplishing multiple objectives for better generalization. We propose a novel multi-task learning model for jointly learning a classifier and a segmentor, from chest X-ray images, through semi-supervised learning. In addition, we propose a new loss function that combines absolute KL divergence with Tversky loss (KLTV) to yield faster convergence and better segmentation performance. Based on our experimental results using a novel segmentation model, an Adversarial Pyramid Progressive Attention U-Net (APPAU-Net), we hypothesize that KLTV can be more effective for generalizing multi-tasking models while being competitive in segmentation-only tasks.

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Section: Experiments and Resultssupporting

confidence: 52%

Semi-supervised Multi-task Learning with Chest X-Ray Images

Imran

Terzopoulos

2019

Machine Learning in Medical Imaging

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“…Considering semantic segmentation of the CXRs, segmentation of the lungs, heart, and clavicle bones is challenging because of the low-quality images and low pixel variation. Previous studies evaluated these issues with preprocessing or deep networks that involve a lot of trainable parameters, creating a computationally expensive CAD solution [23,24]. This study focuses on the accuracy and computational cost for chest anatomy segmentation (lungs, heart, and clavicle bones) for diagnostic purposes.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.

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“…More specifically, the first decoder corresponds to the last encoder, the second decoder corresponds to the penultimate encoder, and so forth. The output of the decoder is fed into a multistage softmax classifier, which conducts classification by using pixels (Mittal, Hooda, & Sofat, 2018).…”

Section: Segnetmentioning

confidence: 99%

Vehicle-Related Scene Understanding Using Deep Learning

Liu

Neuyen

Yan

2020

Communications in Computer and Information Science

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Automated driving technology is an inevitable trend in the future development of transportation, it is also one of the eminent achievements in the matter of artificial intelligence. Primarily deep learning produces a significant contribution to the progression of automatic driving. Deep learning not only promotes autonomous vehicles to sense and identify the surrounding environment, but also identifies and classifies various information regarding to vehicles. With the upgrades and improvement of deep learning technology, it can be promptly and readily learned and employed. A large number of pretraining networks and public datasets have provided convenience for training numerous traffic scenes. Nevertheless, automated driving technology is not flexible enough to understand scenes in complex traffic environments, with regard to traffic rules and transportation facilities in various countries. There is no algorithm so far designed for all traffic scenes. In this thesis, our contributions are that we primarily deal with the issue of understanding of vehicle-related scene using deep learning. To the best of our knowledge, this is the first time that we utilize Auckland traffic environment as an analysis object for scene understanding. Moreover, automatic scene segmentation and object detection are coalesced for traffic scene understanding. The techniques based on deep learning dramatically decrease human manipulations. Furthermore, the datasets in this project provide a large amount of Auckland traffic data. Meanwhile, the performance of CNN processing is consolidated by combining with vehicle detection outcome.

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LF-SegNet: A Fully Convolutional Encoder–Decoder Network for Segmenting Lung Fields from Chest Radiographs

Cited by 65 publications

References 31 publications

Semi-supervised Multi-task Learning with Chest X-Ray Images

Semi-supervised Multi-task Learning with Chest X-Ray Images

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Vehicle-Related Scene Understanding Using Deep Learning

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