Investigations of the Mars Upper Atmosphere with ExoMars Trace Gas Orbiter

The Martian mesosphere and thermosphere, the region above about 60 km, is not the primary target of the ExoMars 2016 mission but its Trace Gas Orbiter (TGO) can explore it and address many interesting issues, either in-situ during the aerobraking period or remotely during the regular mission. In the aerobraking phase TGO peeks into thermospheric densities and temperatures, in a broad range of latitudes and during a long continuous period. TGO carries two instruments designed for the detection of trace species, NOMAD and ACS, which will use the solar occultation technique. Their regular sounding at the terminator up to very high altitudes in many different molecular bands will represent the first time that an extensive and precise dataset of densities and hopefully temperatures are obtained at those altitudes and local times on Mars. But there are additional capabilities in TGO for studying the upper atmosphere of Mars, and we review them briefly. Our simulations suggest that airglow emissions from the UV to the IR might be observed outside the terminator. If eventually confirmed from orbit, they would supply new information about atmospheric dynamics and variability. However, their optimal exploitation requires a special spacecraft pointing, currently not considered in the regular operations but feasible in our opinion. We discuss the synergy between the TGO instruments, specially the wide spectral range achieved by combining them. We also encourage coordinated operations with other Mars-observing missions capable of supplying simultaneous measurements of its upper atmosphere

Visual analytics for the exploration and assessment of segmentation errors

Several diagnostic and treatment procedures require the segmentation of anatomical structures from medical images. However, the automatic model-based methods that are often employed, may produce inaccurate segmentations. These, if used as input for diagnosis or treatment, can have detrimental effects for the patients. Currently, an analysis to predict which anatomic regions are more prone to inaccuracies, and to determine how to improve segmentation algorithms, cannot be performed. We propose a visual tool to enable experts, working on model-based segmentation algorithms, to explore and analyze the outcomes and errors of their methods. Our approach supports the exploration of errors in a cohort of pelvic organ segmentations, where the performance of an algorithm can be assessed. Also, it enables the detailed exploration and assessment of segmentation errors, in individual subjects. To the best of our knowledge, there is no other tool with comparable functionality. A usage scenario is employed to explore and illustrate the capabilities of our visual tool. To further assess the value of the proposed tool, we performed an evaluation with five segmentation experts. The evaluation participants confirmed the potential of the tool in providing new insight into their data and employed algorithms. They also gave feedback for future improvements

Visual analytics for the exploration and assessment of segmentation errors

Several diagnostic and treatment procedures require the segmentation of anatomical structures from medical images. However, the automatic model-based methods that are often employed, may produce inaccurate segmentations. These, if used as input for diagnosis or treatment, can have detrimental effects for the patients. Currently, an analysis to predict which anatomic regions are more prone to inaccuracies, and to determine how to improve segmentation algorithms, cannot be performed. We propose a visual tool to enable experts, working on model-based segmentation algorithms, to explore and analyze the outcomes and errors of their methods. Our approach supports the exploration of errors in a cohort of pelvic organ segmentations, where the performance of an algorithm can be assessed. Also, it enables the detailed exploration and assessment of segmentation errors, in individual subjects. To the best of our knowledge, there is no other tool with comparable functionality. A usage scenario is employed to explore and illustrate the capabilities of our visual tool. To further assess the value of the proposed tool, we performed an evaluation with five segmentation experts. The evaluation participants confirmed the potential of the tool in providing new insight into their data and employed algorithms. They also gave feedback for future improvements