Anandhan Dhanasingh1, Aarno Dietz2, Claude Jolly1, Peter Roland3. 1. MED-EL GmbH, Implants, Innsbruck, Austria. 2. Department of Otolaryngology, Kuopio University Hospital, Kuopio, Finland. 3. Department of Otolaryngology, Universtiy of Texas Southwestern Medical Center, Dallas, USA.
Abstract
OBJECTIVES: Capture the human inner-ear malformation types in 3D by segmenting the inner-ear structures from clinical CT (computed tomography) and MR (magnetic resonance) image datasets. Volumetric analysis was done to find the variations in the volume of cochlear part alone from complete inner-ear followed by 3D printing from the 3D segmented models. MATERIALS AND METHODS: Using 3D slicer freeware, the complete inner-ear structures were segmented from anonymized CT and MR image by setting a tight grey-scale threshold to avoid capturing unwanted structures followed by volumetric analysis of the cochlear part alone. 3D printing was done using Form labs desktop 3D printer. RESULTS: We identified 2x normal anatomy (NA) cochlea, 1x enlarged vestibular aqueduct syndrome (EVAS), 3x incomplete partition (IP) type-I, 4x IP type-II, 3x IP type-III, 5x common cavity (CC) and 5x cochlear hypoplasia (CH). 3D segmented models along with the 3D printed models showed huge variation in size, shape and the anatomy among the image data-sets analyzed. Volumetric analysis showed that on average, volume of CC was above 150mm3, volume of CH fell below 80mm3, Volume of NA, EVAS and IP-I were all around 85-105mm3 whereas the volume of IP-II was around 50mm3. CONCLUSION: Visualizing human inner-ear malformation types in 3D both as computer models and as 3D printed models is a whole-new experience as demonstrated in this study. The volumetric analysis showed a huge variation among the volume of cochlear part alone among the malformation types.
OBJECTIVES: Capture the human inner-ear malformation types in 3D by segmenting the inner-ear structures from clinical CT (computed tomography) and MR (magnetic resonance) image datasets. Volumetric analysis was done to find the variations in the volume of cochlear part alone from complete inner-ear followed by 3D printing from the 3D segmented models. MATERIALS AND METHODS: Using 3D slicer freeware, the complete inner-ear structures were segmented from anonymized CT and MR image by setting a tight grey-scale threshold to avoid capturing unwanted structures followed by volumetric analysis of the cochlear part alone. 3D printing was done using Form labs desktop 3D printer. RESULTS: We identified 2x normal anatomy (NA) cochlea, 1x enlarged vestibular aqueduct syndrome (EVAS), 3x incomplete partition (IP) type-I, 4x IP type-II, 3x IP type-III, 5x common cavity (CC) and 5x cochlear hypoplasia (CH). 3D segmented models along with the 3D printed models showed huge variation in size, shape and the anatomy among the image data-sets analyzed. Volumetric analysis showed that on average, volume of CC was above 150mm3, volume of CH fell below 80mm3, Volume of NA, EVAS and IP-I were all around 85-105mm3 whereas the volume of IP-II was around 50mm3. CONCLUSION: Visualizing human inner-ear malformation types in 3D both as computer models and as 3D printed models is a whole-new experience as demonstrated in this study. The volumetric analysis showed a huge variation among the volume of cochlear part alone among the malformation types.
Authors: Andrew J Fishman; J Thomas Roland; George Alexiades; Jozef Mierzwinski; Noel L Cohen Journal: Otol Neurotol Date: 2003-11 Impact factor: 2.311
Authors: Nora M Weiss; Tabita M Breitsprecher; Alexander Pscheidl; David Bächinger; Stefan Volkenstein; Stefan Dazert; Robert Mlynski; Sönke Langner; Peter Roland; Anandhan Dhanasingh Journal: Eur Arch Otorhinolaryngol Date: 2022-10-10 Impact factor: 3.236
Authors: David Bächinger; Tabita M Breitsprecher; Alexander Pscheidl; Anandhan Dhanasingh; Robert Mlynski; Stefan Dazert; Sönke Langner; Nora M Weiss Journal: Eur Arch Otorhinolaryngol Date: 2022-10-09 Impact factor: 3.236
Authors: Ahmet M Tekin; Marco Matulic; Wim Wuyts; Masoud Zoka Assadi; Griet Mertens; Vincent van Rompaey; Yongxin Li; Paul van de Heyning; Vedat Topsakal Journal: Genes (Basel) Date: 2021-04-21 Impact factor: 4.096
Authors: Anandhan Dhanasingh; Daniel Erpenbeck; Masoud Zoka Assadi; Úna Doyle; Peter Roland; Abdulrahman Hagr; Vincent Van Rompaey; Paul Van de Heyning Journal: Sci Rep Date: 2021-10-21 Impact factor: 4.379
Authors: Laurent Noyalet; Lukas Ilgen; Miriam Bürklein; Wafaa Shehata-Dieler; Johannes Taeger; Rudolf Hagen; Tilmann Neun; Simon Zabler; Daniel Althoff; Kristen Rak Journal: Front Surg Date: 2022-02-04