Liver segmentation from computed tomography images using cascade deep learning.

BACKGROUND: Liver segmentation is a fundamental step in the treatment planning and diagnosis of liver cancer. However, manual segmentation of liver is time-consuming because of the large slice quantity and subjectiveness associated with the specialist's experience, which can lead to segmentation errors. Thus, the segmentation process can be automated using computational methods for better time efficiency and accuracy. However, automatic liver segmentation is a challenging task, as the liver can vary in shape, ill-defined borders, and lesions, which affect its appearance. We aim to propose an automatic method for liver segmentation using computed tomography (CT) images. METHODS: The proposed method, based on deep convolutional neural network models and image processing techniques, comprise of four main steps: (1) image preprocessing, (2) initial segmentation, (3) reconstruction, and (4) final segmentation. RESULTS: We evaluated the proposed method using 131 CT images from the LiTS image base. An average sensitivity of 95.45%, an average specificity of 99.86%, an average Dice coefficient of 95.64%, an average volumetric overlap error (VOE) of 8.28%, an average relative volume difference (RVD) of -0.41%, and an average Hausdorff distance (HD) of 26.60 mm were achieved. CONCLUSIONS: This study demonstrates that liver segmentation, even when lesions are present in CT images, can be efficiently performed using a cascade approach and including a reconstruction step based on deep convolutional neural networks.

Esophagus segmentation from planning CT images using an atlas-based deep learning approach.

BACKGROUND AND OBJECTIVE: One of the main steps in the planning of radiotherapy (RT) is the segmentation of organs at risk (OARs) in Computed Tomography (CT). The esophagus is one of the most difficult OARs to segment. The boundaries between the esophagus and other surrounding tissues are not well-defined, and it is presented in several slices of the CT. Thus, manually segment the esophagus requires a lot of experience and takes time. This difficulty in manual segmentation combined with fatigue due to the number of slices to segment can cause human errors. To address these challenges, computational solutions for analyzing medical images and proposing automated segmentation have been developed and explored in recent years. In this work, we propose a fully automatic method for esophagus segmentation for better planning of radiotherapy in CT. METHODS: The proposed method is a fully automated segmentation of the esophagus, consisting of 5 main steps: (a) image acquisition; (b) VOI segmentation; (c) preprocessing; (d) esophagus segmentation; and (e) segmentation refinement. RESULTS: The method was applied in a database of 36 CT acquired from 3 different institutes. It achieved the best results in literature so far: Dice coefficient value of 82.15%, Jaccard Index of 70.21%, accuracy of 99.69%, sensitivity of 90.61%, specificity of 99.76%, and Hausdorff Distance of 6.1030 mm. CONCLUSIONS: With the achieved results, we were able to show how promising the method is, and that applying it in large medical centers, where esophagus segmentation is still an arduous and challenging task, can be of great help to the specialists.

Kidney segmentation from computed tomography images using deep neural network.

BACKGROUND: The precise segmentation of kidneys and kidney tumors can help medical specialists to diagnose diseases and improve treatment planning, which is highly required in clinical practice. Manual segmentation of the kidneys is extremely time-consuming and prone to variability between different specialists due to their heterogeneity. Because of this hard work, computational techniques, such as deep convolutional neural networks, have become popular in kidney segmentation tasks to assist in the early diagnosis of kidney tumors. In this study, we propose an automatic method to delimit the kidneys in computed tomography (CT) images using image processing techniques and deep convolutional neural networks (CNNs) to minimize false positives. METHODS: The proposed method has four main steps: (1) acquisition of the KiTS19 dataset, (2) scope reduction using AlexNet, (3) initial segmentation using U-Net 2D, and (4) false positive reduction using image processing to maintain the largest elements (kidneys). RESULTS: The proposed method was evaluated in 210 CTs from the KiTS19 database and obtained the best result with an average Dice coefficient of 96.33%, an average Jaccard index of 93.02%, an average sensitivity of 97.42%, an average specificity of 99.94% and an average accuracy of 99.92%. In the KiTS19 challenge, it presented an average Dice coefficient of 93.03%. CONCLUSION: In our method, we demonstrated that the kidney segmentation problem in CT can be solved efficiently using deep neural networks to define the scope of the problem and segment the kidneys with high precision and with the use of image processing techniques to reduce false positives.

An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks.

BACKGROUND AND OBJECTIVE: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical component in any computer-aided system, for either detection or diagnosis in digital CXR, is the automatic segmentation of the lung field. One of the main challenges inherent to this task is to include in the segmentation the lung regions overlapped by dense abnormalities, also known as opacities, which can be caused by diseases such as tuberculosis and pneumonia. This specific task is difficult because opacities frequently reach high intensity values which can be incorrectly interpreted by an automatic method as the lung boundary, and as a consequence, this creates a challenge in the segmentation process, because the chances of incomplete segmentations are increased considerably. The purpose of this work is to propose a method for automatic segmentation of lungs in CXR that addresses this problem by reconstructing the lung regions "lost" due to pulmonary abnormalities. METHODS: The proposed method, which features two deep convolutional neural network models, consists of four steps main steps: (1) image acquisition, (2) initial segmentation, (3) reconstruction and (4) final segmentation. RESULTS: The proposed method was experimented on 138 Chest X-ray images from Montgomery County's Tuberculosis Control Program, and has achieved as best result an average sensitivity of 97.54%, an average specificity of 96.79%, an average accuracy of 96.97%, an average Dice coefficient of 94%, and an average Jaccard index of 88.07%. CONCLUSIONS: We demonstrate in our lung segmentation method that the problem of dense abnormalities in Chest X-rays can be efficiently addressed by performing a reconstruction step based on a deep convolutional neural network model.