3 research outputs found
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
MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection
In this study, we propose a lung nodule detection scheme which fully
incorporates the clinic workflow of radiologists. Particularly, we exploit
Bi-Directional Maximum intensity projection (MIP) images of various thicknesses
(i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10
adjacent slices to feed into self-distillation-based Multi-Encoders Network
(MEDS-Net). The proposed architecture first condenses 3D patch input to three
channels by using a dense block which consists of dense units which effectively
examine the nodule presence from 2D axial slices. This condensed information,
along with the forward and backward MIP images, is fed to three different
encoders to learn the most meaningful representation, which is forwarded into
the decoded block at various levels. At the decoder block, we employ a
self-distillation mechanism by connecting the distillation block, which
contains five lung nodule detectors. It helps to expedite the convergence and
improves the learning ability of the proposed architecture. Finally, the
proposed scheme reduces the false positives by complementing the main detector
with auxiliary detectors. The proposed scheme has been rigorously evaluated on
888 scans of LUNA16 dataset and obtained a CPM score of 93.6\%. The results
demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to
effectively distinguish nodules from surroundings which help to achieve the
sensitivity of 91.5% and 92.8% with false positives rate of 0.25 and 0.5 per
scan, respectively
Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans
Accurate segmentation of mandibular canals in lower jaws is important in dental implantology. Medical experts manually determine the implant position and dimensions from 3D CT images to avoid damaging the mandibular nerve inside the canal. In this paper, we propose a novel dual-stage deep learning-based scheme for the automatic segmentation of the mandibular canal. In particular, we first enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme, which improves the visibility of mandibular canals. After enhancement, we designed 3D deeply supervised attention UNet architecture for localizing the Volumes Of Interest (VOIs), which contain the mandibular canals (i.e., left and right canals). Finally, we employed the Multi-Scale input Residual UNet (MSiR-UNet) architecture to segment the mandibular canals using VOIs accurately. The proposed method has been rigorously evaluated on 500 and 15 CBCT scans from our dataset and from the public dataset, respectively. The results demonstrate that our technique improves the existing performance of mandibular canal segmentation to a clinically acceptable range. Moreover, it is robust against the types of CBCT scans in terms of field of view