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This research presents a novel multifunctional platform focusing on the clinical diagnosis of kidneys and their pathology (tumors, stones and cysts), using a &#x201C;templates&#x201D;-based technique. As a first step, specialist clinicians train the system by accurately annotating the kidneys and their abnormalities creating &#x201C;3-D golden standard models.&#x201D; Then, medical technicians experimentally adjust rules and parameters (stored as &#x201C;templates&#x201D;) for the integrated &#x201C;automatic recognition framework&#x201D; to achieve results which are closest to those of the clinicians. These parameters can later be used by nonexperts to achieve increased automation in the identification process. The system's functionality was tested on 20 MRI datasets (552 images), while the &#x201C;automatic 3-D models&#x201D; created were validated against the &#x201C;3-D golden standard models.&#x201D; Results are promising as they yield an average accuracy of 97.2% in successfully identifying kidneys and 96.1% of their abnormalities thus outperforming existing methods both in accuracy and in processing time needed.

ATD: A Multiplatform for Semiautomatic 3-D Detection of Kidneys and Their Pathology in Real Time