Computer-aided Detection of the Most Suitable MRI Sequences for Subsequent Spinal Metastasis Delineation

Detection and segmentation of vertebral metastases is a crucial step for support of diagnosis and treatment planning, especially in minimally invasive interventions. Even though computer-assistant tools will not dispense radiologists yet, algorithmically supported detection and segmentation of spinal metastases will play a more and more important role in the near future. The usage of images, where a sufficiently good differentiation between metastases and surrounding tissue is possible, constitutes a critical requirement for successful segmentation procedures. Therefore, we proposed a pipeline, that semi-automatically sorts out unsuitable imaging sequences, as well as combinations of different images via absolute intensity difference images and returns a ranking based recommendation of which image data fits best the requirements for future segmentation tasks. We evaluated our method with 10 patient cases and matched the produced ranking with those of a segmentation field expert. With an average Spearman’s ranking coefficient of 0.92±0.07, our method showed promising results and could be a valueable pre-processing step to speed up clinical segmentation procedures due to omitting the time-consuming manual initialization of choosing suitable image data.