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Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion
Author(s) -
Luu Ha Manh,
Walsum Theo,
Franklin Daniel,
Pham Phuong Cam,
Vu Luu Dang,
Moelker Adriaan,
Staring Marius,
VanHoang Xiem,
Niessen Wiro,
Trung Nguyen Linh
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14814
Subject(s) - artificial intelligence , computer science , anisotropic diffusion , lossless compression , image compression , computer vision , convolutional neural network , pattern recognition (psychology) , image quality , compression ratio , data compression , image processing , image (mathematics) , internal combustion engine , automotive engineering , engineering
Purpose Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ‐specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter. Methods The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC‐visually lossless, is applied to compress the image. We demonstrate the proposed method on three‐dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak‐signal‐to‐noise ratio ( PSNR ), structural similarity ( SSIM ), and compression ratio ( CR ) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images. Results The results show that the method can significantly improve CR of most well‐known compression methods. DLAD combined with HEVC‐visually lossless achieves the highest average CR of 6.45, which is 36% higher than that of the original HEVC and outperforms other state‐of‐the‐art lossless medical image compression methods. The means of PSNR and SSIM are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation. Conclusions We thus conclude that the method has a high potential to be applied in teleintervention applications.