A multitask deep learning approach for pulmonary embolism detection and identification

Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold standard for PE diagnoses. Deep learning can enhance the radiologists’workflow by identifying PE using CTPA, which helps to prioritize important cases and hasten the diagnoses for at-risk patients. In this study, we propose a two-phase multitask learning method that can recognize the presence of PE and its properties such as the position, whether acute or chronic, and the corresponding right-to-left ventricle diameter (RV/LV) ratio, thereby reducing false-negative diagnoses. Trained on the RSNA-STR Pulmonary Embolism CT Dataset, our model demonstrates promising PE detection performances on the hold-out test set with the window-level AUROC achieving 0.93 and the sensitivity being 0.86 with a specificity of 0.85, which is competitive with the radiologists’sensitivities ranging from 0.67 to 0.87 with specificities of 0.89–0.99. In addition, our model provides interpretability through attention weight heatmaps and gradient-weighted class activation mapping (Grad-CAM). Our proposed deep learning model could predict PE existence and other properties of existing cases, which could be applied to practical assistance for PE diagnosis

Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation

Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a 3D image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and 94% . Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification

A two step deep learning workflow for pumonary embolism segmentation and classification

Orientador: Lucas Ferrari de OliveiraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 14/12/2021Inclui referências: p. 57-60Área de concentração: Ciência da ComputaçãoResumo: Embolia pulmonar está entre as principais causas de morte no mundo todo, de acordo com o Datasus 22% dos pacientes hospitalizados por embolia pulmonar acabam falecendo, trata-se de um trombo alojado em alguma região da vascularização arterial pulmonar. Para estabelecer o tratamento adequado e reduzir a mortalidade associada é necessário um diagnóstico rápido por parte da equipe médica, atualmente a forma de diagnóstico mais utilizado é a analise de imagens de tomografia computadorizada, devido a vários fatores como a sua velocidade de aquisição, alta disponibilidade dos equipamentos que fazem a sua captura e uma alta acurácia no diagnóstico. Um exame de tomografia computadorizada é composto de centenas de imagens que requerem a atenção do radiologista, pelo número alto de dados produzidos a análise de tais exames pode ser cansativa e levar a erros de diagnóstico devido a fadiga, ainda hoje a embolia pulmonar está entre as doenças onde se há mais erros diagnósticos. Nos últimos anos alguns sistemas computacionais de auxílio ao diagnóstico foram desenvolvidos para auxiliar radiologistas na detecção de trombos, tais sistemas têm se tornado de grande ajuda para um diagnóstico mais eficaz. Deep learning tem sido um dos tópicos mais comentados na área da visão computacional ultimamente, especialmente na área do processamento de imagens médicas, mais especificamente em aplicações de detecção e reconhecimento de imagens. Isso também se aplica no auxílio ao diagnóstico de embolia pulmonar, alguns trabalhos têm atingido resultados do estado da arte utilizando modelos complexos de redes neurais, que são capazes de identificar trombos dentro das imagens geradas pela tomografia, removendo outros ruídos que podem ser vistos como um falso positivo. O objetivo deste trabalho é desenvolver uma aplicação de deep learning capaz de encontrar tromboembolismos pulmonares em imagens de tomografias computadorizadas, a robustez do modelo permitirá que detecte trombos em exames de diferente origens. Se bem sucedido, o algoritmo produzido neste trabalho será capaz de auxiliar radiologistas em um diagnóstico rápido com uma alta probabilidade de acerto. Alguns testes preliminares já mostram que modelos de deep learning são capazes de discriminar embolias pulmonares, em uma base de dados pública contendo imagens de tomografia computadorizada de pulmão a rede foi capaz de encontrar vários trombos. Com um total de 35 exames, 28 foram usados para treinar o modelo e validar seus resultados, ajustando seus hiperparâmetros de acordo com os resultados, as outras 7 imagens foram utilizadas como teste, avaliado como o sistema se comporta quando recebe dados reais, atingindo um Dice score de 0.81 e uma acurácia de 84%, apesar de já apresentar bons resultados a modelo ainda possui espaço para melhores, pois ainda há diversos métodos de otimização que costumam melhorar os resultados das arquiteturas.Abstract: Pulmonary embolism is one of the leading causes of death all over the world, according to Datasus the mortality rate of patients hospitalized due to pulmonary embolism is 22%. To break the clot and save the patient a fast diagnosis is required, that is the reason why computer tomography is used as a means to detect embolisms. A computed tomography exam is composed of hundreds of images that require an analysis from a radiologist, due to the high number of images this process can be tiresome and can lead to errors due to fatigue, pulmonary embolism remains one of the frequent misdiagnosis due to this fact. Over the years some computed aided systems had been developed aiming to help radiologists to see some missed clot, those systems had proven to be of great aid to an even faster diagnosis. Deep learning models have been increased significantly in many computer vision problems, especially in medical imaging, in image detection and recognition. This is also true in the classification of pulmonary embolisms, some works achieve a state of the art results by applying complex neural network models that can identify a clot from a whole tomography exam and remove any potential false positive found. The purpose of this work is to develop a deep learning application that is capable of discriminate pulmonary embolisms from a whole computed tomography volume, due to the use of deep learning a robust model can be developed that can generalize the process of embolism detection in different sources of data. If successful, this work will be able to aid radiologists in a fast diagnosis of pulmonary embolisms with a high discrimination probability. Some preliminary tests show that a deep learning architecture can discriminate pulmonary embolisms, a public dataset was used for validation of this architecture and can find several clots. With a total of 35 exams, 28 were used for training the model and validating its results, tweaking the models' hyperparameters with the results, the last 7 exams were used for testing the model, simulating how it should behave in a receiving unknown data, it achieves a Dice score of 0.81 and an accuracy of 84%, even if it got a relatively good result, it got plenty of room for improvement still, since many known improvement methods can still be applied in the architecture

SEMIAUTOMATIC DETECTION OF STENOSIS AND OCCLUSION OF PULMONARY ARTERIES FOR PATIENTS WITH CHRONIC THROMBOEMBOLIC PULMONARY HYPERTENSION

Chronic thromboembolic pulmonary hypertension (CTEPH) is a severe lung disease defined by the presence of chronic blood clots in the pulmonary arteries accompanied by severe health complications. It is necessary to go through a large set of axial sections from Computed tomography pulmonary angiogram (CTPA) for diagnosing the disease, which is difficult and time consuming for the radiologist. The radiologist's experience plays a significant role, same as subjective factors such as attention and fatigue. In this work we pursued the design and development of the algorithm for semiautomatic detection of pulmonary artery stenoses and clots for diagnosing CTEPH, which is based on the implementation of semantic segmentation using deep convolutional neural networks. Specifically, it is about the use of the DeepLab V3 + model embedded in the Xception architecture. Within this work we focused on stenoses and clots located in larger pulmonary arteries. Anonymized data of patients diagnosed with CTEPH and one healthy patient in the term of the presence of the disease were used for realization of this work. Statistical analysis of the results is divided into two parts: analysis of the created algorithm based on comparison of outputs with ground truth data (manually marked references) and analysis of pathology detection on new data based on comparison of predictions with reference images from the radiologist. The proposed algorithm correctly detects present vascular pathology in 83% of cases (sensitivity) and precisely selects cases where the investigated pathology does not occur in 72% of cases (specificity). The calculated Matthews correlation coefficient is 0.53. This means that the predictive ability of the algorithm is moderate positive. The designed and developed image analysis algorithm offers the radiologist a "second opinion" and it also could enable to increase the sensitivity of CTEPH diagnostics in cooperation with a radiologist.