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Mahbod, Amirreza
Publications (2 of 2) Show all publications
Mahbod, A., Chowdhury, M., Smedby, Ö. & Wang, C. (2018). Automatic brain segmentation using artificial neural networks with shape context. Pattern Recognition Letters, 101, 74-79
Open this publication in new window or tab >>Automatic brain segmentation using artificial neural networks with shape context
2018 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 101, p. 74-79Article in journal (Refereed) Published
Abstract [en]

Segmenting brain tissue from MR scans is thought to be highly beneficial for brain abnormality diagnosis, prognosis monitoring, and treatment evaluation. Many automatic or semi-automatic methods have been proposed in the literature in order to reduce the requirement of user intervention, but the level of accuracy in most cases is still inferior to that of manual segmentation. We propose a new brain segmentation method that integrates volumetric shape models into a supervised artificial neural network (ANN) framework. This is done by running a preliminary level-set based statistical shape fitting process guided by the image intensity and then passing the signed distance maps of several key structures to the ANN as feature channels, in addition to the conventional spatial-based and intensity-based image features. The so-called shape context information is expected to help the ANN to learn local adaptive classification rules instead of applying universal rules directly on the local appearance features. The proposed method was tested on a public datasets available within the open MICCAI grand challenge (MRBrainS13). The obtained average Dice coefficient were 84.78%, 88.47%, 82.76%, 95.37% and 97.73% for gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), brain (WM + GM) and intracranial volume respectively. Compared with other methods tested on the same dataset, the proposed method achieved competitive results with comparatively shorter training time.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Medical Image Processing
urn:nbn:se:kth:diva-219889 (URN)10.1016/j.patrec.2017.11.016 (DOI)000418101400011 ()2-s2.0-85036471005 (Scopus ID)

QC 20171215

Available from: 2017-12-15 Created: 2017-12-15 Last updated: 2019-10-28Bibliographically approved
Commowick, O., Istace, A., Kain, M., Laurent, B., Leray, F., Simon, M., . . . Barillot, C. (2018). Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure. Scientific Reports, 8, Article ID 13650.
Open this publication in new window or tab >>Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
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2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 13650Article in journal (Refereed) Published
Abstract [en]

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning,.), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
National Category
Other Engineering and Technologies
urn:nbn:se:kth:diva-235440 (URN)10.1038/s41598-018-31911-7 (DOI)000444374900001 ()30209345 (PubMedID)2-s2.0-85053246791 (Scopus ID)

QC 20180927

Available from: 2018-09-27 Created: 2018-09-27 Last updated: 2018-10-09Bibliographically approved

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