Automatic segmentation of the core of the acoustic radiation in humans
IntroductionAcoustic radiation is one of the most important white matter fiber bundles of the human auditory system. However, segmenting the acoustic radiation is challenging due to its small size and proximity to several larger fiber bundles. TractSeg is a method that uses a neural network to segme...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2022.934650/full |
| _version_ | 1798001470487920640 |
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| author | Malin Siegbahn Malin Siegbahn Cecilia Engmér Berglin Cecilia Engmér Berglin Rodrigo Moreno |
| author_facet | Malin Siegbahn Malin Siegbahn Cecilia Engmér Berglin Cecilia Engmér Berglin Rodrigo Moreno |
| author_sort | Malin Siegbahn |
| collection | DOAJ |
| description | IntroductionAcoustic radiation is one of the most important white matter fiber bundles of the human auditory system. However, segmenting the acoustic radiation is challenging due to its small size and proximity to several larger fiber bundles. TractSeg is a method that uses a neural network to segment some of the major fiber bundles in the brain. This study aims to train TractSeg to segment the core of acoustic radiation.MethodsWe propose a methodology to automatically extract the acoustic radiation from human connectome data, which is both of high quality and high resolution. The segmentation masks generated by TractSeg of nearby fiber bundles are used to steer the generation of valid streamlines through tractography. Only streamlines connecting the Heschl's gyrus and the medial geniculate nucleus were considered. These streamlines are then used to create masks of the core of the acoustic radiation that is used to train the neural network of TractSeg. The trained network is used to automatically segment the acoustic radiation from unseen images.ResultsThe trained neural network successfully extracted anatomically plausible masks of the core of the acoustic radiation in human connectome data. We also applied the method to a dataset of 17 patients with unilateral congenital ear canal atresia and 17 age- and gender-paired controls acquired in a clinical setting. The method was able to extract 53/68 acoustic radiation in the dataset acquired with clinical settings. In 14/68 cases, the method generated fragments of the acoustic radiation and completely failed in a single case. The performance of the method on patients and controls was similar.DiscussionIn most cases, it is possible to segment the core of the acoustic radiations even in images acquired with clinical settings in a few seconds using a pre-trained neural network. |
| first_indexed | 2024-04-11T11:36:45Z |
| format | Article |
| id | doaj.art-65027de458e84067b7b6f476bda110f4 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-11T11:36:45Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-65027de458e84067b7b6f476bda110f42022-12-22T04:25:56ZengFrontiers Media S.A.Frontiers in Neurology1664-22952022-09-011310.3389/fneur.2022.934650934650Automatic segmentation of the core of the acoustic radiation in humansMalin Siegbahn0Malin Siegbahn1Cecilia Engmér Berglin2Cecilia Engmér Berglin3Rodrigo Moreno4Division of Ear, Nose and Throat Diseases, Department of Clinical Science, Intervention and Technology, Karolinska Institute, Stockholm, SwedenMedical Unit Ear, Nose, Throat and Hearing, Karolinska University Hospital, Stockholm, SwedenDivision of Ear, Nose and Throat Diseases, Department of Clinical Science, Intervention and Technology, Karolinska Institute, Stockholm, SwedenMedical Unit Ear, Nose, Throat and Hearing, Karolinska University Hospital, Stockholm, SwedenDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, SwedenIntroductionAcoustic radiation is one of the most important white matter fiber bundles of the human auditory system. However, segmenting the acoustic radiation is challenging due to its small size and proximity to several larger fiber bundles. TractSeg is a method that uses a neural network to segment some of the major fiber bundles in the brain. This study aims to train TractSeg to segment the core of acoustic radiation.MethodsWe propose a methodology to automatically extract the acoustic radiation from human connectome data, which is both of high quality and high resolution. The segmentation masks generated by TractSeg of nearby fiber bundles are used to steer the generation of valid streamlines through tractography. Only streamlines connecting the Heschl's gyrus and the medial geniculate nucleus were considered. These streamlines are then used to create masks of the core of the acoustic radiation that is used to train the neural network of TractSeg. The trained network is used to automatically segment the acoustic radiation from unseen images.ResultsThe trained neural network successfully extracted anatomically plausible masks of the core of the acoustic radiation in human connectome data. We also applied the method to a dataset of 17 patients with unilateral congenital ear canal atresia and 17 age- and gender-paired controls acquired in a clinical setting. The method was able to extract 53/68 acoustic radiation in the dataset acquired with clinical settings. In 14/68 cases, the method generated fragments of the acoustic radiation and completely failed in a single case. The performance of the method on patients and controls was similar.DiscussionIn most cases, it is possible to segment the core of the acoustic radiations even in images acquired with clinical settings in a few seconds using a pre-trained neural network.https://www.frontiersin.org/articles/10.3389/fneur.2022.934650/fullacoustic radiationdiffusion MRItractographyTractSegdeep learning |
| spellingShingle | Malin Siegbahn Malin Siegbahn Cecilia Engmér Berglin Cecilia Engmér Berglin Rodrigo Moreno Automatic segmentation of the core of the acoustic radiation in humans Frontiers in Neurology acoustic radiation diffusion MRI tractography TractSeg deep learning |
| title | Automatic segmentation of the core of the acoustic radiation in humans |
| title_full | Automatic segmentation of the core of the acoustic radiation in humans |
| title_fullStr | Automatic segmentation of the core of the acoustic radiation in humans |
| title_full_unstemmed | Automatic segmentation of the core of the acoustic radiation in humans |
| title_short | Automatic segmentation of the core of the acoustic radiation in humans |
| title_sort | automatic segmentation of the core of the acoustic radiation in humans |
| topic | acoustic radiation diffusion MRI tractography TractSeg deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2022.934650/full |
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