Technical Note: plastimatch mabs, an open source tool for automatic image segmentation. Issue 9 (23rd August 2016)

• Record Type: Journal Article
• Authors: Zaffino, Paolo; Raudaschl, Patrik; Fritscher, Karl; Sharp, Gregory C.; Spadea, Maria Francesca
• Is Part Of: Medical physics. Volume 43:Issue 9(2016)
• Page Start: 5155
• Page End: 5160
• Publication Date: 2016-08-23
• Subjects: biomedical MRI -- brain -- computerised tomography -- image registration -- image segmentation -- medical image processing -- neurophysiology -- public domain software -- radiation therapy; MRI: anatomic, functional, spectral, diffusion -- Registration -- Segmentation -- Computed tomography -- Clinical applications -- Therapeutic applications, including brachytherapy; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Computerised tomographs -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; automatic segmentation -- multiatlas based segmentation -- CT -- MRI -- open source; Computer software -- Medical image segmentation -- Optic nerve structure -- Databases -- Neuroscience -- Image registration -- Computed tomography -- Radiotherapy sources; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4961121
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: 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 introduceplastimatch 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.