35 research outputs found
Kits19 segmentation challenge
In this work we briefly describe our methodology for the kidney segmentation problem
A Sequential Framework for Detection and Classification of Abnormal Teeth in Panoramic X-rays
This paper describes our solution for the Dental Enumeration and Diagnosis on
Panoramic X-rays Challenge at MICCAI 2023. Our approach consists of a
multi-step framework tailored to the task of detecting and classifying abnormal
teeth. The solution includes three sequential stages: dental instance
detection, healthy instance filtering, and abnormal instance classification. In
the first stage, we employed a Faster-RCNN model for detecting and identifying
teeth. In subsequent stages, we designed a model that merged the encoding
pathway of a pretrained U-net, optimized for dental lesion detection, with the
Vgg16 architecture. The resulting model was first used for filtering out
healthy teeth. Then, any identified abnormal teeth were categorized,
potentially falling into one or more of the following conditions: embedded,
periapical lesion, caries, deep caries. The model performing dental instance
detection achieved an AP score of 0.49. The model responsible for identifying
healthy teeth attained an F1 score of 0.71. Meanwhile, the model trained for
multi-label dental disease classification achieved an F1 score of 0.76. The
code is available at
https://github.com/tudordascalu/2d-teeth-detection-challenge
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
Teeth localization, segmentation, and labeling from intra-oral 3D scans are
essential tasks in modern dentistry to enhance dental diagnostics, treatment
planning, and population-based studies on oral health. However, developing
automated algorithms for teeth analysis presents significant challenges due to
variations in dental anatomy, imaging protocols, and limited availability of
publicly accessible data. To address these challenges, the 3DTeethSeg'22
challenge was organized in conjunction with the International Conference on
Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022,
with a call for algorithms tackling teeth localization, segmentation, and
labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans
from 900 patients was prepared, and each tooth was individually annotated by a
human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this
dataset. In this study, we present the evaluation results of the 3DTeethSeg'22
challenge. The 3DTeethSeg'22 challenge code can be accessed at:
https://github.com/abenhamadou/3DTeethSeg22_challengeComment: 29 pages, MICCAI 2022 Singapore, Satellite Event, Challeng