Definiens in Medical Imaging

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2 The Challenge in Medical Imaging Medical imaging helps to detect, identify and observe tumors over time. Screening, for example, is the first line of defense against breast cancer. CT and MRI scans are vital diagnostic tools. Biopsies help to determine whether lesions are malignant or benign. And, once treatment has begun, medical imaging is one of the most powerful tools for measuring therapeutic response. The proliferation of image data in oncology has created some major challenges. First and foremost, there is a global shortage of trained radiologists and histopathologists. Without them, the increasing number of scans and tissue samples cannot be analyzed, creating an ‘image analysis bottleneck’ that delays the diagnosis and treatment of patients. Secondly, the task is very complex. Due to the inherent variability of living organisms, even the most detailed scans can be difficult to interpret. As we strive to detect a cancer ever earlier, many tumors are so small and escape easy detection. The Journal for Thoracic Imaging noted, in a study evaluating the detection of small pulmonary nodules, that: “Observer detection of pulmonary nodules in the range of 5-15mm using current digital radiography systems is not reliable in the confusing environment of the lung.” 1 Spotting lesions and tumors requires concentration, training and a very good eye. In this challenging interpretive environment, human error is compounded by fatigue and an overload of data. Taken together, these challenges delay detection, diagnosis and treatment.This causes unnecessary patient suffering and results in more costly and less effective treatment. Screening Diagnostic Staging Therapy Follow-up e.g. Mammography e.g. CT Colonoscopy e.g. Mammography e.g. CT Colonoscopy e.g. Whole Body MR Figure 1 Patient diagnostic and treatment paradigm Early diagnosis starts with screening, followed by diagnostic tests if suspicious lesions are found. In the case of malignant findings the staging phase will be used to size and locate tumors which may be treated with surgical, chemical or radiation therapy or a combination. Following the treatment a “re-staging” evaluates the success of therapy. Definiens in Medical Imaging 1 Wu, N, et al.Vol. 21 (1): pp27-31. March 2006. Available at: www.ncbi.nlm.nih.gov/pubmed/16538152.
Medical Imaging in Oncology Definiens’ software can be used to complement or improve any existing imaging system. It is easily incorporated into current systems to provide an enhanced level of image intelligence. This enables medical professionals to detect cancer earlier and supports them in making the best decisions to provide personalized treatment for patients. The benefits in oncology Definiens’ technology is ideally suited to screen, identify, diagnose and treat cancer in four ways. The technology increases sensitivity, improves consistency, increases productivity, and accurately measures the volume of tumors helping to assess the efficacy of treatment. As a result, Definiens’ technology helps clinicians and specialists to identify tumors earlier, make better treatment decisions and assess the efficacy of treatment more accurately. The cost of treatment is reduced and patients benefit from earlier intervention and improved treatment. Example: Automatic segmentation of the liver In 20 datasets, representing a large variation of pathological cases, the liver was automatically segmented. In each case, the resulting segmentation was evaluated using the manual annotation generated jointly by two medical experts. Definiens’ technology achieved an average volumetric error of 7.6% and an average mean surface distance of 2.73mm. In view of the short prototype development time (less than 30 days) the performance was good and several issues were observed for future development. A potential source of error is lesions, which may be initially misclassified as air or fat and therefore not subject to the “dark object inclusion” process. Both these issues have been addressed in a later version of the software. The potential of Definiens Cognition Network Technology® to deliver auto mated, precise and robust 3D detection of liver was demonstrated even in pathological cases. The abi li ty to incorporate explicit domain knowledge and context in a simple form is unique and, in our opinion, a mandatory feature for the development of complex solutions. These solutions relieve radiologists of many mundane image analysis tasks and enable them to focus precious time on the diagnosis and treatment of patients. Figure 2 Screenshot showing analysis of 20 Liver Samples. a) Directory with 20 images, b) Transversal 2D display, detected liver segments outlined in red, c) Process hierarchy of anatomical model, d) 3D visualization of segmented liver, e) Information on selected liver image Definiens in Medical Imaging 3
Page 1: Understanding Images DEEPER INSIGHT
Page 5 and 6: Information Centric Healthcare One
Page 7 and 8: Better Decisions Faster Results Dee

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