Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography
White matter tractography mapping is an important tool for neuro-surgical planning and navigation. It relies on the accurate manual delineation of anatomical seeding ROIs (sROIs) by neuroanatomy experts. Stringent pre-operative time-constraints and limited availability of experts suggest that automa...
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| Veröffentlicht in: | IEEE transactions on medical imaging 2020-05, Vol.39 (5), p.1655-1667 |
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| creator | Avital, Itzik Nelkenbaum, Ilya Tsarfaty, Galia Konen, Eli Kiryati, Nahum Mayer, Arnaldo |
| description | White matter tractography mapping is an important tool for neuro-surgical planning and navigation. It relies on the accurate manual delineation of anatomical seeding ROIs (sROIs) by neuroanatomy experts. Stringent pre-operative time-constraints and limited availability of experts suggest that automation tools are strongly needed for the task. In this article, we propose and compare several multi-modal fully convolutional network architectures for segmentation of sROIs. Inspired by their manual segmentation practice, anatomical information from T1w maps is fused by the network with directionally encoded color (DEC) maps to compute the segmentation. Qualitative and quantitative validation was performed on image data from 75 real tumor resection candidates for the sROIs of the motor tract, the arcuate fasciculus, and optic radiation. Favorable comparison was also obtained with state-of-the-art methods for the tumor dataset as well as the ISMRM 2017 traCED challenge dataset. The proposed networks showed promising results, indicating they may significantly improve the efficiency of pre-surgical tractography mapping, without compromising its quality. |
| doi_str_mv | 10.1109/TMI.2019.2954477 |
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It relies on the accurate manual delineation of anatomical seeding ROIs (sROIs) by neuroanatomy experts. Stringent pre-operative time-constraints and limited availability of experts suggest that automation tools are strongly needed for the task. In this article, we propose and compare several multi-modal fully convolutional network architectures for segmentation of sROIs. Inspired by their manual segmentation practice, anatomical information from T1w maps is fused by the network with directionally encoded color (DEC) maps to compute the segmentation. Qualitative and quantitative validation was performed on image data from 75 real tumor resection candidates for the sROIs of the motor tract, the arcuate fasciculus, and optic radiation. Favorable comparison was also obtained with state-of-the-art methods for the tumor dataset as well as the ISMRM 2017 traCED challenge dataset. 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| subjects | Anatomy Automation brain Brain architecture Brain mapping Computer architecture Datasets Fully convolutional neural networks Image segmentation Manuals Mapping Metastatic seeding multi-modal segmentation Planning Qualitative analysis Radiation Substantia alba Surgical instruments Task analysis Three-dimensional displays tractography Tumors |
| title | Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography |
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