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Early diagnosis of pulmonary embolism using deep learning and SPECT/CT Exams Fusion

International Journal of Radiology & Radiation Therapy
Hamida Romdhane,1 Mohamed Ali Cherni,2 Dorra Ben Sellem3


Background: Pulmonary embolism is a serious disease, which can be life-threatening. Its treatment and its detection are sometimes complicated. The two most commonly used imaging techniques are the computed tomography pulmonary angiography and the pulmonary scintigraphy. Currently, hybrid imagery, combining single photon emission computerized tomography and computed tomography (SPECT/CT), play an important role in the diagnosis of pulmonary embolism. Objective: Our aim, in the analytical study, is to detect the pulmonary embolism. A whole new method based on the fusion of SPECT and CT images by the deep Siamese Neural Network is proposed to early detect this fatal disease. Material and methods: This method consists of two main parts: fusion of SPECT and CT images and detection of the pathological lobes. It starts with the segmentation of both SPECT and CT images to obtain 3D binary images. Next, we detect the different pulmonary lobes in the CT images. Then, we merge the two SPECT and CT images by deep Siamese Neural Network. Afterward, they are compared to the image where the different lobes are identified to finally detect the pulmonary embolism and identify the pathological lobes. Results: The validation achieved an accuracy of 90.5%, with a sensitivity of 88.2% and a specificity of 91.5%. Conclusion: The obtained results prove the effectiveness of the proposed method in the detection of pulmonary embolism.


image fusion, image processing, deep siamese neural network, SPECT/CT, pulmonary lobes