Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

In the present paper, a method for the detection of malignant and benign tumors on the CT scan images has been proposed. In the proposed method, firstly the area of interest in which the tumor may exist is selected on the original image and by the use of image segmentation and determination of the image’s threshold limit, the tumor’s area is specified and then edge detection filters are used for detection of the tumor’s edge. After detection of area and by calculating the fractal dimensions with less percent of errors and better resolution, the areas where contain the tumor are determined. The images used in the proposed method have been extracted from cancer imaging archive database that is made available for public. Compared to other methods, our proposed method recognizes successfully benign and malignant tumors in all cases that have been clinically approved and belong to the database

Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks

Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish from vascular and bone structures using CXR. Computer vision has previously been proposed to assist human radiologists in this task, however, leading studies use down-sampled images and computationally expensive methods with unproven generalization. Instead, this study localizes lung nodules using efficient encoder-decoder neural networks that process full resolution images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested using the JSRT lung nodule dataset. The networks are used to localize lung nodules from an independent external CXR dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and spatial filtering, but with a sub-second inference time that is faster than other methods. The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7.6

Computer-assisted diagnosis for an early identification of lung cancer in chest X rays

Lung cancer; X-rays; Computer-assisted diagnosisCáncer de pulmón; Rayos X; Diagnóstico asistido por computadoraCàncer de pulmó; Raigs X; Diagnòstic assistit per ordinadorComputer-assisted diagnosis (CAD) algorithms have shown its usefulness for the identification of pulmonary nodules in chest x-rays, but its capability to diagnose lung cancer (LC) is unknown. A CAD algorithm for the identification of pulmonary nodules was created and used on a retrospective cohort of patients with x-rays performed in 2008 and not examined by a radiologist when obtained. X-rays were sorted according to the probability of pulmonary nodule, read by a radiologist and the evolution for the following three years was assessed. The CAD algorithm sorted 20,303 x-rays and defined four subgroups with 250 images each (percentiles ≥ 98, 66, 33 and 0). Fifty-eight pulmonary nodules were identified in the ≥ 98 percentile (23,2%), while only 64 were found in lower percentiles (8,5%) (p < 0.001). A pulmonary nodule was confirmed by the radiologist in 39 out of 173 patients in the high-probability group who had follow-up information (22.5%), and in 5 of them a LC was diagnosed with a delay of 11 months (12.8%). In one quarter of the chest x-rays considered as high-probability for pulmonary nodule by a CAD algorithm, the finding is confirmed and corresponds to an undiagnosed LC in one tenth of the cases

Classificação de nódulos pulmonares baseada em redes neurais convolucionais profundas em radiografias

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O câncer de pulmão, que se caracteriza pela presença de nódulos, é o tipo mais comum de câncer em todo o mundo, além de ser um dos mais agressivos e fatais, com 20% da mortalidade total por câncer. A triagem do câncer de pulmão pode ser realizada por radiologistas que analisam imagens de raios-X de tórax (CXR). No entanto, a detecção de nódulos pulmonares é uma tarefa difícil devido a sua grande variabilidade, limitações humanas de memória, distração e fadiga, entre outros fatores. Essas dificuldades motivam o desenvolvimento de sistemas de diagnóstico por computador (CAD) para apoiar radiologistas na detecção de nódulos pulmonares. A classificação do nódulo do pulmão é um dos principais tópicos relacionados aos sistemas de CAD. Embora as redes neurais convolucionais (CNN) tenham demonstrado um bom desempenho em muitas tarefas, há poucas explorações de seu uso para classificar nódulos pulmonares em imagens CXR. Neste trabalho, propusemos e analisamos um arcabouço para a detecção de nódulos pulmonares em imagens de CXR que inclui segmentação da área pulmonar, localização de nódulos e classificação de nódulos candidatos. Apresentamos um método para classificação de nódulos candidatos com CNNs treinadas a partir do zero. A eficácia do nosso método baseia-se na seleção de parâmetros de aumento de dados, no projeto de uma arquitetura CNN especializada, no uso da regularização de dropout na rede, inclusive em camadas convolucionais, e no tratamento da falta de amostras de nódulos em comparação com amostras de fundo, balanceando mini-lotes em cada iteração da descida do gradiente estocástico. Todas as decisões de seleção do modelo foram tomadas usando-se um subconjunto de imagens CXR da base Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) separadamente. Então, utilizamos todas as imagens com nódulos no conjunto de dados da Japanese Society of Radiological Technology (JSRT) para avaliação. Nossos experimentos mostraram que as CNNs foram capazes de alcançar resultados competitivos quando comparados com métodos da literatura. Nossa proposta obteve uma curva de operação (AUC) de 7.51 considerando 10 falsos positivos por imagem (FPPI) e uma sensibilidade de 71.4% e 81.0% com 2 e 5 FPPI, respectivamenteAbstract: Lung cancer, which is characterized by the presence of nodules, is the most common type of cancer around the world, as well as one of the most aggressive and deadliest cancer, with 20% of total cancer mortality. Lung cancer screening can be performed by radiologists analyzing chest X-ray (CXR) images. However, the detection of lung nodules is a difficult task due to their wide variability, human limitations of memory, distraction and fatigue, among other factors. These difficulties motivate the development of computer-aided diagnosis (CAD) systems for supporting radiologists in detecting lung nodules. Lung nodule classification is one of the main topics related to CAD systems. Although convolutional neural networks (CNN) have been demonstrated to perform well on many tasks, there are few explorations of their use for classifying lung nodules in CXR images. In this work, we proposed and analyzed a pipeline for detecting lung nodules in CXR images that includes lung area segmentation, potential nodule localization, and nodule candidate classification. We presented a method for classifying nodule candidates with a CNN trained from the scratch. The effectiveness of our method relies on the selection of data augmentation parameters, the design of a specialized CNN architecture, the use of dropout regularization on the network, inclusive in convolutional layers, and addressing the lack of nodule samples compared to background samples balancing mini-batches on each stochastic gradient descent iteration. All model selection decisions were taken using a CXR subset of the Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset separately. Thus, we used all images with nodules in the Japanese Society of Radiological Technology (JSRT) dataset for evaluation. Our experiments showed that CNNs were capable of achieving competitive results when compared to state-of-the-art methods. Our proposal obtained an area under the free-response receiver operating characteristic (AUC) curve of 7.51 considering 10 false positives per image (FPPI), and a sensitivity of 71.4% and 81.0% with 2 and 5 FPPI, respectivelyMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Relative Merits of 3D Visualization for the Detection of Subtle Lung Nodules

A new imaging modality called bi-plane correlation imaging (BCI) was examined to determine the merits of using BCI with stereoscopic visualization to detect subtle lung nodules. In the first aim of this project, the optimal geometry for conventional projection imaging applications was assessed using a theoretical model to develop generic results for MTF, NNPS, eDQE. The theoretical model was tested with a clinical system using two magnifications and two anthropomorphic chest phantoms to assess the modalities of single view CXR and stereo/BCI. Results indicated that magnification can potentially improve the signal and noise performance of digital images. Results also demonstrated that a cross over point occurs in the spatial frequency above and below which the effects of magnification differ indicating that there are task dependent tradeoffs associated with magnification. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by 30-40%. For both anthropomorphic phantoms, at iso-dose, eDQE(0) for stereo/BCI was ~100 times higher than that for CXR. Magnification at iso-dose improved eDQE(0) by ~10 times for BCI. Increasing the dose did not improve results. The findings indicated that stereo/BCI with magnification may improve detection of subtle lung nodules compared to single view CXR. With quantitative results in place, a pilot clinical trial was constructed. Human subject data was acquired with a BCI acquisition system. Subjects were imaged in the PA position as well as two oblique angles. Realistic simulated lesions were added to a subset of subjects determined to be nodule free. A BCI CAD algorithm was also applied. In randomized readings, radiologists read the cases according to viewing protocol. For the radiologist trainees, the AUC of lesion detection was seen to improve by 2.8% (p &lt; 0.05) for stereoscopic viewing after monoscopic viewing compared to monoscopic viewing only. A 13% decrease in false positives was observed. Stereo/BCI as an adjunct modality was beneficial. However, the full potential of stereo/BCI as a replacement modality for single view chest x-ray may be realized with improved observer training, clinically relevant stereoscopic displays, and more challenging detection tasks.Doctor of Philosoph