title: Technical Note: plastimatch mabs, an open source tool for automatic image segmentation.
abstract: OBJECTIVE
  Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included in some commercial platforms for radiotherapy planning and surgery guidance. Anyway, to date, a software with no restrictions about the anatomical district and image modality is still missing. In this paper we introduce plastimatch mabs, an open source software that can be used with any image modality for automatic segmentation.
  
  
  METHODS
  plastimatch mabs workflow consists of two main parts: (1) an offline phase, where optimal registration and voting parameters are tuned and (2) an online phase, where a new patient is labeled from scratch by using the same parameters as identified in the former phase. Several registration strategies, as well as different voting criteria can be selected. A flexible atlas selection scheme is also available. To prove the effectiveness of the proposed software across anatomical districts and image modalities, it was tested on two very different scenarios: head and neck (H&N) CT segmentation for radiotherapy application, and magnetic resonance image brain labeling for neuroscience investigation.
  
  
  RESULTS
  For the neurological study, minimum dice was equal to 0.76 (investigated structures: left and right caudate, putamen, thalamus, and hippocampus). For head and neck case, minimum dice was 0.42 for the most challenging structures (optic nerves and submandibular glands) and 0.62 for the other ones (mandible, brainstem, and parotid glands). Time required to obtain the labels was compatible with a real clinical workflow (35 and 120 min).
  
  
  CONCLUSIONS
  The proposed software fills a gap in the multiatlas based segmentation field, since all currently available tools (both for commercial and for research purposes) are restricted to a well specified application. Furthermore, it can be adopted as a platform for exploring MABS parameters and as a reference implementation for comparing against other segmentation algorithms.
authors: Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro 88100, Italy.
authors: Paolo
authors: P
authors: Zaffino
authors: Institute for Biomedical Image Analysis, Private University of Health Sciences, Medical Informatics and Technology, Hall in Tirol 6060, Austria.
authors: Patrik
authors: P
authors: Raudaschl
authors: Institute for Biomedical Image Analysis, Private University of Health Sciences, Medical Informatics and Technology, Hall in Tirol 6060, Austria.
authors: Karl
authors: K
authors: Fritscher
authors: Department for Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114.
authors: Gregory C
authors: GC
authors: Sharp
authors: Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro 88100, Italy.
authors: Maria Francesca
authors: MF
authors: Spadea
mesh_terms: D000465:Algorithms; D001331:Automation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012984:Software; D014057:Tomography, X-Ray Computed