1,206 research outputs found
Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement
Individual tooth segmentation and identification from cone-beam computed
tomography images are preoperative prerequisites for orthodontic treatments.
Instance segmentation methods using convolutional neural networks have
demonstrated ground-breaking results on individual tooth segmentation tasks,
and are used in various medical imaging applications. While point-based
detection networks achieve superior results on dental images, it is still a
challenging task to distinguish adjacent teeth because of their similar
topologies and proximate nature. In this study, we propose a point-based tooth
localization network that effectively disentangles each individual tooth based
on a Gaussian disentanglement objective function. The proposed network first
performs heatmap regression accompanied by box regression for all the
anatomical teeth. A novel Gaussian disentanglement penalty is employed by
minimizing the sum of the pixel-wise multiplication of the heatmaps for all
adjacent teeth pairs. Subsequently, individual tooth segmentation is performed
by converting a pixel-wise labeling task to a distance map regression task to
minimize false positives in adjacent regions of the teeth. Experimental results
demonstrate that the proposed algorithm outperforms state-of-the-art approaches
by increasing the average precision of detection by 9.1%, which results in a
high performance in terms of individual tooth segmentation. The primary
significance of the proposed method is two-fold: 1) the introduction of a
point-based tooth detection framework that does not require additional
classification and 2) the design of a novel loss function that effectively
separates Gaussian distributions based on heatmap responses in the point-based
detection framework.Comment: 11 pages, 7 figure
Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning
Accurate quantification of pulmonary nodules can greatly assist the early
diagnosis of lung cancer, which can enhance patient survival possibilities. A
number of nodule segmentation techniques have been proposed, however, all of
the existing techniques rely on radiologist 3-D volume of interest (VOI) input
or use the constant region of interest (ROI) and only investigate the presence
of nodule voxels within the given VOI. Such approaches restrain the solutions
to investigate the nodule presence outside the given VOI and also include the
redundant structures into VOI, which may lead to inaccurate nodule
segmentation. In this work, a novel semi-automated approach for 3-D
segmentation of nodule in volumetric computerized tomography (CT) lung scans
has been proposed. The proposed technique can be segregated into two stages, at
the first stage, it takes a 2-D ROI containing the nodule as input and it
performs patch-wise investigation along the axial axis with a novel adaptive
ROI strategy. The adaptive ROI algorithm enables the solution to dynamically
select the ROI for the surrounding slices to investigate the presence of nodule
using deep residual U-Net architecture. The first stage provides the initial
estimation of nodule which is further utilized to extract the VOI. At the
second stage, the extracted VOI is further investigated along the coronal and
sagittal axis with two different networks and finally, all the estimated masks
are fed into the consensus module to produce the final volumetric segmentation
of nodule. The proposed approach has been rigorously evaluated on the LIDC
dataset, which is the largest publicly available dataset. The result suggests
that the approach is significantly robust and accurate as compared to the
previous state of the art techniques.Comment: The manuscript is currently under review and copyright shall be
transferred to the publisher upon acceptanc
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