111 research outputs found
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks
Quantitative assessment of the abdominal region from clinically acquired CT
scans requires the simultaneous segmentation of abdominal organs. Thanks to the
availability of high-performance computational resources, deep learning-based
methods have resulted in state-of-the-art performance for the segmentation of
3D abdominal CT scans. However, the complex characterization of organs with
fuzzy boundaries prevents the deep learning methods from accurately segmenting
these anatomical organs. Specifically, the voxels on the boundary of organs are
more vulnerable to misprediction due to the highly-varying intensity of
inter-organ boundaries. This paper investigates the possibility of improving
the abdominal image segmentation performance of the existing 3D encoder-decoder
networks by leveraging organ-boundary prediction as a complementary task. To
address the problem of abdominal multi-organ segmentation, we train the 3D
encoder-decoder network to simultaneously segment the abdominal organs and
their corresponding boundaries in CT scans via multi-task learning. The network
is trained end-to-end using a loss function that combines two task-specific
losses, i.e., complete organ segmentation loss and boundary prediction loss. We
explore two different network topologies based on the extent of weights shared
between the two tasks within a unified multi-task framework. To evaluate the
utilization of complementary boundary prediction task in improving the
abdominal multi-organ segmentation, we use three state-of-the-art
encoder-decoder networks: 3D UNet, 3D UNet++, and 3D Attention-UNet. The
effectiveness of utilizing the organs' boundary information for abdominal
multi-organ segmentation is evaluated on two publically available abdominal CT
datasets. A maximum relative improvement of 3.5% and 3.6% is observed in Mean
Dice Score for Pancreas-CT and BTCV datasets, respectively.Comment: 15 pages, 16 figures, journal pape
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
Medical image segmentation methods often rely on fully supervised approaches
to achieve excellent performance, which is contingent upon having an extensive
set of labeled images for training. However, annotating medical images is both
expensive and time-consuming. Semi-supervised learning offers a solution by
leveraging numerous unlabeled images alongside a limited set of annotated ones.
In this paper, we introduce a semi-supervised medical image segmentation method
based on the mean-teacher model, referred to as Dual-Decoder Consistency via
Pseudo-Labels Guided Data Augmentation (DCPA). This method combines consistency
regularization, pseudo-labels, and data augmentation to enhance the efficacy of
semi-supervised segmentation. Firstly, the proposed model comprises both
student and teacher models with a shared encoder and two distinct decoders
employing different up-sampling strategies. Minimizing the output discrepancy
between decoders enforces the generation of consistent representations, serving
as regularization during student model training. Secondly, we introduce mixup
operations to blend unlabeled data with labeled data, creating mixed data and
thereby achieving data augmentation. Lastly, pseudo-labels are generated by the
teacher model and utilized as labels for mixed data to compute unsupervised
loss. We compare the segmentation results of the DCPA model with six
state-of-the-art semi-supervised methods on three publicly available medical
datasets. Beyond classical 10\% and 20\% semi-supervised settings, we
investigate performance with less supervision (5\% labeled data). Experimental
outcomes demonstrate that our approach consistently outperforms existing
semi-supervised medical image segmentation methods across the three
semi-supervised settings
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation
In recent years, continuous latent space (CLS) and discrete latent space
(DLS) deep learning models have been proposed for medical image analysis for
improved performance. However, these models encounter distinct challenges. CLS
models capture intricate details but often lack interpretability in terms of
structural representation and robustness due to their emphasis on low-level
features. Conversely, DLS models offer interpretability, robustness, and the
ability to capture coarse-grained information thanks to their structured latent
space. However, DLS models have limited efficacy in capturing fine-grained
details. To address the limitations of both DLS and CLS models, we propose
SynergyNet, a novel bottleneck architecture designed to enhance existing
encoder-decoder segmentation frameworks. SynergyNet seamlessly integrates
discrete and continuous representations to harness complementary information
and successfully preserves both fine and coarse-grained details in the learned
representations. Our extensive experiment on multi-organ segmentation and
cardiac datasets demonstrates that SynergyNet outperforms other state of the
art methods, including TransUNet: dice scores improving by 2.16%, and Hausdorff
scores improving by 11.13%, respectively. When evaluating skin lesion and brain
tumor segmentation datasets, we observe a remarkable improvement of 1.71% in
Intersection-over Union scores for skin lesion segmentation and of 8.58% for
brain tumor segmentation. Our innovative approach paves the way for enhancing
the overall performance and capabilities of deep learning models in the
critical domain of medical image analysis.Comment: Accepted at WACV 202
FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images
Computed Tomography (CT) based precise prostate segmentation for treatment
planning is challenging due to (1) the unclear boundary of prostate derived
from CTs poor soft tissue contrast, and (2) the limitation of convolutional
neural network based models in capturing long-range global context. Here we
propose a focal transformer based image segmentation architecture to
effectively and efficiently extract local visual features and global context
from CT images. Furthermore, we design a main segmentation task and an
auxiliary boundary-induced label regression task as regularization to
simultaneously optimize segmentation results and mitigate the unclear boundary
effect, particularly in unseen data set. Extensive experiments on a large data
set of 400 prostate CT scans demonstrate the superior performance of our focal
transformer to the competing methods on the prostate segmentation task.Comment: 13 pages, 3 figures, 2 table
Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm
Medical image segmentation based on deep learning is often faced with the
problems of insufficient datasets and long time-consuming labeling. In this
paper, we introduce the self-supervised method MAE(Masked Autoencoders) into
knee joint images to provide a good initial weight for the segmentation model
and improve the adaptability of the model to small datasets. Secondly, we
propose a weakly supervised paradigm for meniscus segmentation based on the
combination of point and line to reduce the time of labeling. Based on the weak
label ,we design a region growing algorithm to generate pseudo-label. Finally
we train the segmentation network based on pseudo-labels with weight transfer
from self-supervision. Sufficient experimental results show that our proposed
method combining self-supervision and weak supervision can almost approach the
performance of purely fully supervised models while greatly reducing the
required labeling time and dataset size.Comment: 8 pages,10 figure
Class Activation Mapping and Uncertainty Estimation in Multi-Organ Segmentation
Deep learning (DL)-based medical imaging and image segmentation algorithms achieve impressive performance on many benchmarks. Yet the efficacy of deep learning methods for future clinical applications may become questionable due to the lack of ability to reason with uncertainty and interpret probable areas of failures in prediction decisions. Therefore, it is desired that such a deep learning model for segmentation classification is able to reliably predict its confidence measure and map back to the original imaging cases to interpret the prediction decisions. In this work, uncertainty estimation for multiorgan segmentation task is evaluated to interpret the predictive modeling in DL solutions. We use the state-of-the-art nnU-Net to perform segmentation of 15 abdominal organs (spleen, right kidney, left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, pancreas, right adrenal gland, left adrenal gland, duodenum, bladder, prostate/uterus) using 200 patient cases for the Multimodality Abdominal Multi-Organ Segmentation Challenge 2022. Further, the softmax probabilities from different variants of nnU-Net are used to compute the knowledge uncertainty in the deep learning framework. Knowledge uncertainty from ensemble of DL models is utilized to quantify and visualize class activation map for two example segmented organs. The preliminary result of our model shows that class activation maps may be used to interpret the prediction decision made by the DL model used in this study
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