35 research outputs found
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI
Prostate cancer is the second leading cause of cancer death among men in the
United States. The diagnosis of prostate MRI often relies on the accurate
prostate zonal segmentation. However, state-of-the-art automatic segmentation
methods often fail to produce well-contained volumetric segmentation of the
prostate zones since certain slices of prostate MRI, such as base and apex
slices, are harder to segment than other slices. This difficulty can be
overcome by accounting for the cross-slice relationship of adjacent slices, but
current methods do not fully learn and exploit such relationships. In this
paper, we propose a novel cross-slice attention mechanism, which we use in a
Transformer module to systematically learn the cross-slice relationship at
different scales. The module can be utilized in any existing learning-based
segmentation framework with skip connections. Experiments show that our
cross-slice attention is able to capture the cross-slice information in
prostate zonal segmentation and improve the performance of current
state-of-the-art methods. Our method significantly improves segmentation
accuracy in the peripheral zone, such that the segmentation results are
consistent across all the prostate slices (apex, mid-gland, and base)
FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
This contribution presents a deep learning method for the segmentation of
prostate zones in MRI images based on U-Net using additive and feature pyramid
attention modules, which can improve the workflow of prostate cancer detection
and diagnosis. The proposed model is compared to seven different U-Net-based
architectures. The automatic segmentation performance of each model of the
central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were
evaluated using Dice Score (DSC), and the Intersection over Union (IoU)
metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of
76.9% in the test set, outperforming most of the studied models in this work
except from R2U-Net and attention R2U-Net architectures.Comment: This paper has been accepted at the 22nd Mexican International
Conference on Artificial Intelligence (MICAI 2023
A Subabdominal MRI Image Segmentation Algorithm Based on Multi-Scale Feature Pyramid Network and Dual Attention Mechanism
This study aimed to solve the semantic gap and misalignment issue between
encoding and decoding because of multiple convolutional and pooling operations
in U-Net when segmenting subabdominal MRI images during rectal cancer
treatment. A MRI Image Segmentation is proposed based on a multi-scale feature
pyramid network and dual attention mechanism. Our innovation is the design of
two modules: 1) a dilated convolution and multi-scale feature pyramid network
are used in the encoding to avoid the semantic gap. 2) a dual attention
mechanism is designed to maintain spatial information of U-Net and reduce
misalignment. Experiments on a subabdominal MRI image dataset show the proposed
method achieves better performance than others methods. In conclusion, a
multi-scale feature pyramid network can reduce the semantic gap, and the dual
attention mechanism can make an alignment of features between encoding and
decoding.Comment: 19 pages,9 figure
Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review
Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this modality of treatment is both fundamental, time-consuming and prone to human-errors, leading to potentially avoidable delays in start of treatment. A fundamental step in radiotherapy planning is contouring of radiation targets, where medical specialists contouring, i.e., segment, the boundaries of the structures to be irradiated. Automating this step can potentially lead to faster treatment planning without a decrease in quality, while increasing time available to physicians and also more consistent treatment results. This can be framed as an image segmentation task, which has been studied for many decades in the fields of Computer Vision and Machine Learning. With the advent of Deep Learning, there have been many proposals for different network architectures achieving high performance levels. In this review, we searched the literature for those methods and describe them briefly, grouping those based on Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). This is a booming field, evidenced by the date of the publications found. However, most publications use data from a very limited number of patients, which presents an obstacle to deep learning models training. Although the performance of the models has achieved very satisfactory results, there is still room for improvement, and there is arguably a long way before these models can be used safely and effectively in clinical practice. (c) 2020, Springer Science+Business Media, LLC, part of Springer Nature
Attention Mechanisms in Medical Image Segmentation: A Survey
Medical image segmentation plays an important role in computer-aided
diagnosis. Attention mechanisms that distinguish important parts from
irrelevant parts have been widely used in medical image segmentation tasks.
This paper systematically reviews the basic principles of attention mechanisms
and their applications in medical image segmentation. First, we review the
basic concepts of attention mechanism and formulation. Second, we surveyed over
300 articles related to medical image segmentation, and divided them into two
groups based on their attention mechanisms, non-Transformer attention and
Transformer attention. In each group, we deeply analyze the attention
mechanisms from three aspects based on the current literature work, i.e., the
principle of the mechanism (what to use), implementation methods (how to use),
and application tasks (where to use). We also thoroughly analyzed the
advantages and limitations of their applications to different tasks. Finally,
we summarize the current state of research and shortcomings in the field, and
discuss the potential challenges in the future, including task specificity,
robustness, standard evaluation, etc. We hope that this review can showcase the
overall research context of traditional and Transformer attention methods,
provide a clear reference for subsequent research, and inspire more advanced
attention research, not only in medical image segmentation, but also in other
image analysis scenarios.Comment: Submitted to Medical Image Analysis, survey paper, 34 pages, over 300
reference
U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical
image analysis that can precisely segment images using a scarce amount of
training data. These traits provide U-net with a very high utility within the
medical imaging community and have resulted in extensive adoption of U-net as
the primary tool for segmentation tasks in medical imaging. The success of
U-net is evident in its widespread use in all major image modalities from CT
scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a
segmentation tool, there have been instances of the use of U-net in other
applications. As the potential of U-net is still increasing, in this review we
look at the various developments that have been made in the U-net architecture
and provide observations on recent trends. We examine the various innovations
that have been made in deep learning and discuss how these tools facilitate
U-net. Furthermore, we look at image modalities and application areas where
U-net has been applied.Comment: 42 pages, in IEEE Acces
Deep learning enables prostate mri segmentation: a large cohort evaluation with inter-rater variability analysis
Whole-prostate gland (WPG) segmentation plays a significant role in prostate volume measurement, treatment, and biopsy planning. This study evaluated a previously developed automatic WPG segmentation, deep attentive neural network (DANN), on a large, continuous patient cohort to test its feasibility in a clinical setting. With IRB approval and HIPAA compliance, the study cohort included 3,698 3T MRI scans acquired between 2016 and 2020. In total, 335 MRI scans were used to train the model, and 3,210 and 100 were used to conduct the qualitative and quantitative evaluation of the model. In addition, the DANN-enabled prostate volume estimation was evaluated by using 50 MRI scans in comparison with manual prostate volume estimation. For qualitative evaluation, visual grading was used to evaluate the performance of WPG segmentation by two abdominal radiologists, and DANN demonstrated either acceptable or excellent performance in over 96% of the testing cohort on the WPG or each prostate sub-portion (apex, midgland, or base). Two radiologists reached a substantial agreement on WPG and midgland segmentation (κ=0.75 and 0.63) and moderate agreement on apex and base segmentation (κ=0.56 and 0.60). For quantitative evaluation, DANN demonstrated a dice similarity coefficient of 0.93±0.02, significantly higher than other baseline methods, such as Deeplab v3+ and UNet (both p values < 0.05). For the volume measurement, 96% of the evaluation cohort achieved differences between the DANN-enabled and manual volume measurement within 95% limits of agreement. In conclusion, the study showed that the DANN achieved sufficient and consistent WPG segmentation on a large, continuous study cohort, demonstrating its great potential to serve as a tool to measure prostate volume