Dataset Information

Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks.

ABSTRACT: Generating accurate 3D models from cone-beam computed tomography (CBCT) images is an important step in developing treatment plans for patients with craniomaxillofacial (CMF) deformities. This process often involves bone segmentation and landmark digitization. Since anatomical landmarks generally lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly correlated. However, most existing methods simply treat them as two standalone tasks, without considering their inherent association. In addition, these methods usually ignore the spatial context information (i.e., displacements from voxels to landmarks) in CBCT images. To this end, we propose a context-guided fully convolutional network (FCN) for joint bone segmentation and landmark digitization. Specifically, we first train an FCN to learn the displacement maps to capture the spatial context information in CBCT images. Using the learned displacement maps as guidance information, we further develop a multi-task FCN to jointly perform bone segmentation and landmark digitization. Our method has been evaluated on 107 subjects from two centers, and the experimental results show that our method is superior to the state-of-the-art methods in both bone segmentation and landmark digitization.

SUBMITTER: Zhang J

PROVIDER: S-EPMC5786437 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks.

Zhang Jun J Liu Mingxia M Wang Li L Chen Si S Yuan Peng P Li Jianfu J Shen Steve Guo-Fang SG Tang Zhen Z Chen Ken-Chung KC Xia James J JJ Shen Dinggang D

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 20170904

Generating accurate 3D models from cone-beam computed tomography (CBCT) images is an important step in developing treatment plans for patients with craniomaxillofacial (CMF) deformities. This process often involves bone segmentation and landmark digitization. Since anatomical landmarks generally lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly correlated. However, most existing methods simply treat them as two standalone ta ...[more]

PMID: 29376150

Similar Datasets

Project description:External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to computed tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3D convolutional neural network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the five networks and the final segmentation was generated by voting on the obtained five candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0??±??3.4?mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60??±??0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy.

Dataset Information

Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks.

Publications

Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets