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SUMMARY:Run in seconds on your laptop: A lightweight model for CT segmenta
 tion of maxillary sinuses in children with chronic rhinosinusitis
DTSTART;VALUE=DATE-TIME:20260909T074000Z
DTEND;VALUE=DATE-TIME:20260909T081000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191400Z
UID:indico-contribution-2023@events.ncbj.gov.pl
DESCRIPTION:Speakers: Przemysław Olbratowski (Biological and Chemical Res
 earch Centre\, University of Warsaw)\nWe present a compact 3D neural netwo
 rk for segmentation of maxillary sinuses from CT scans in children with ch
 ronic rhinosinusitis. It comprises 12 convolutional layers totaling 50k pa
 rameters\, minimal compared to most 3D medical-imaging architectures\, and
  completes the prediction in a few seconds on an average CPU\, making the 
 tool accessible for most clinicians. The sinus volume and its fraction occ
 upied by inflammatory changes are estimated with an RMSE of 0.5 cm$^3$ and
  1.5 %pts\, respectively\, which allows for precise longitudinal monitorin
 g. The network includes no dropout\, no pooling\, and no padding\, the lat
 ter preventing layer-wise injection of meaningless zeros. We utilize a cus
 tom normalization that does not average the data but employs the same runn
 ing statistics in both prediction and training\, making the model insensit
 ive to overall contrast and local artifacts. The network is fully convolut
 ional and translation-invariant. It has an inner receptive field of 18x18x
 18 voxels to detect sinus walls and an outer receptive field of 88x88x88 v
 oxels to provide a broader context. The internal data flow is designed to 
 minimize the number of mappings that the network must learn. It operates i
 n a reduced resolution of 1x1x1 mm$^3$\, which still allows for high preci
 sion thanks to the use of fuzzy labels accounting for the partial-volume e
 ffect. The model was trained and tested on a dataset of 92 scans collected
  and manually annotated specifically for this study.\n\nhttps://events.ncb
 j.gov.pl/event/468/contributions/2023/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2023/
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