scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, .

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal "Modified BSD" open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image.

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal "Modified BSD" open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image.

Our method enables fast, cost-effective and reproducible quantification of ADPKD progression that will facilitate and lower the costs of clinical trials in ADPKD and other disorders requiring accurate, longitudinal kidney quantification. In addition, it will hasten the routine use of TKV as a prognostic biomarker in ADPKD.

Purpose Non-invasive imaging techniques that quantify renal tissue composition are needed to more accurately ascertain prognosis and monitor disease progression in polycystic kidney disease (PKD). Given the success of Magnetization Transfer (MT) imaging to characterize various tissue remodeling pathologies, it was tested on a murine model of autosomal dominant PKD. Methods C57Bl/6 Pkd1 R3277C mice at 9, 12, and 15-months were imaged with a 16.4T MR imaging system. Images were acquired without and with RF saturation in order to calculate MT ratio (MTR) maps. Following imaging, the mice were euthanized and kidney sections were analyzed for cystic and fibrotic indices which were compared to statistical parameters of the MTR maps. Results MTR derived mean, median, 25th percentile, skewness, and kurtosis were all closely related to indices of renal pathology such as kidney/body weight, cystic index, and percent of remaining parenchyma. The correlation between MTR and histology derived cystic and fibrotic changes was good at R2 = 0.84, and R2 = 0.70, respectively. Conclusion MT imaging provides a new, non-invasive means to measure tissue remodeling changes of PKD and may be better suited for characterizing renal impairment compared with conventional MR techniques.