SU-G-IeP2-14: Validation of Plastimatch MABS, a Tool for Automatic Image Segmentation




In this work we present the validation of Plastimatch MABS, an open source software for multi atlas based segmentation of medical images.


The validation was performed on two different clinical datasets: 1) 25 CT image volumes of patients treated for H&N cancer; 2) 20 MRI series of patients having a neurological diagnosis. For the first set, 8 organs at risk to be spared during the therapy were segmented: mandible, brainstem and both left/right optic nerves, parotid and submandibular glands. For the neurological set, 4 paired brain structures were segmented: left/right caudate, putamen, thalamus and hippocampus. For both cases a leave one out approach was used for validation. Tests were performed on common hardware (Intel Xeon CPU @ 2.5 GHz, 8 GB of RAM).


Segmentation accuracy was quantified in terms of Dice Similarity Coefficient (DSC) and 95th percentile of Hausdorff Distance (HD). The average DSCs computed over all the labels were 0.70 for H&N application and 0.85 for neuroscience case. HD values confirm the DSCs (4.92 mm and 1.41 mm). Average segmentation time for the two applications was 35 and 120 mins respectively.


Plastimatch MABS has been validated for multi atlas based segmentation of medical images. Accuracy was demonstrated for different anatomical districts and image modalities. The results show that Plastimatch MABS can be used for a wide range of applications. The presented approach can provide fully automatic and robust segmentation and is also compatible with timing requirements for clinical applications.