Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network.

Hepatic echinococcosis (HE) is a life-threatening liver disease caused by parasites that requires a precise diagnosis and proper treatments. To assess HE lesions accurately, we propose a novel automatic HE lesion segmentation and classification network that contains lesion region positioning (LRP) and lesion region segmenting (LRS) modules. First, we used the LRP module to obtain the probability map of the lesion distribution and the position of the lesion. Then, based on the result of the LRP module, we used the LRS module to precisely segment the HE lesions within the high-probability region. Finally, we classified the HE lesions and identified the lesion types by a convolutional neural network (CNN). The entire dataset was delineated by the hospital's senior radiologist. We collected CT slices of 160 patients from Qinghai Provincial People's Hospital. The Dice score of the final segmentation result reached 89.89%. The Dice scores, indicating the classification accuracy, for cystic vs. alveolar echinococcosis and calcified vs. noncalcified lesions were 80.32% and 82.45%, the sensitivities were 72.41% and 75.17%, the specificities were 83.72% and 86.04%, the NPVs were 80.01% and 86.96%, the PPVs were 80.45% and 81.74%, and the areas under the ROC curves were 0.8128 and 0.8205, respectively. Graphical abstract.

Accurate and rapid CT image segmentation of the eyes and surrounding organs for precise radiotherapy.

OBJECTIVE: The precise segmentation of organs at risk (OARs) is of importance for improving therapeutic outcomes and reducing injuries of patients undergoing radiotherapy. In this study, we developed a new approach for accurate computed tomography (CT) image segmentation of the eyes and surrounding organs, which is first locating then segmentation (FLTS). METHODS: The FLTS approach was composed of two steps: (a) classification of CT images using convolutional neural networks (CNN), and (b) segmentation of the eyes and surrounding organs using modified U-shape networks. In order to obtain optimal performance, we enhanced our training datasets by random jitter and rotation. RESULTS: This model was trained and verified using the clinical datasets that were delineated by experienced physicians. The dice similarity coefficient (DSC) was employed to evaluate the performance of our segmentation method. The average DSCs for the segmentation of the pituitary, left eye, right eye, left eye lens, right eye lens, left optic nerve, and right optic nerve were 90%, 94%, 93.5%, 84.5%, 84.3%, 80.3%, and 82.2%, respectively. CONCLUSION: We developed a new network-based approach for rapid and accurate CT image segmentation of the eyes and surrounding organs. This method is accurate and efficient, and is suitable for clinical use.