Dear Colleagues,

All anatomical structures in the human body, such as organs, bones, and muscles, are three-dimensional objects with a defined shape, and some diseases may alter that shape. As humans, we can detect shapes and indeed shape change because evolution has equipped us with spatial vision. Within the diagnosis process, we underutilize our spatial vision when we look at two-dimensional medical images. The argument for humans working with two dimensional images is centered on standardization and data reduction. For example, oncologists train to recognize cancer texture in medical images. They use this ability to measure tumor cross-sections on specific MRI slices. This operation condenses all the data from the MRI measurement corpus into a single standardized number which is easy to handle by human experts. Unfortunately, information is lost during that operation. Most current computer-aided diagnosis procedures mimic this approach by considering only texture features from a specific image. This approach has two conceptional shortcomings. The first of these shortcomings results from the fact that computing machines are capable of handling and processing large data volumes because they are not limited by the human perception system. Hence, computers can interpret the shape of relevant objects, such as tumors, and shape change caused by specific diseases based on three-dimensional image data. The second shortcoming arises from the selection process, which determines the specific image of interest. In many cases, that process relies on human decision making, where a clinical expert selects one cross-sectional image from a 3D measurement corpus. Inevitably, executing this choice introduces inter- and intra-observer variability. Furthermore, involving human expertise early on in the analysis goes against the goal of reducing workload through computer-aided diagnosis. For this Special Issue, we are interested in studies that push the boundaries of science and technology by offering computer-aided diagnosis based on machine-extractable knowledge from the shape of anatomical structures.

Dr. Oliver Faust
Dr. U. Rajendra Acharya
Prof. Dr. Li Su
Guest Editors

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• medical image processing
• computer-aided diagnosis
• artificial intelligence
• 3D imaging
• hybrid decision support
• magnetic resonance imaging
• computed tomography
• positron emission tomography
• ultrasound

• Machine Extractable Knowledge from the Shape of Anatomical Structures in Diagnostics (11 articles)