345 research outputs found
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
We propose Boundary-RL, a novel weakly supervised segmentation method that
utilises only patch-level labels for training. We envision the segmentation as
a boundary detection problem, rather than a pixel-level classification as in
previous works. This outlook on segmentation may allow for boundary delineation
under challenging scenarios such as where noise artefacts may be present within
the region-of-interest (ROI) boundaries, where traditional pixel-level
classification-based weakly supervised methods may not be able to effectively
segment the ROI. Particularly of interest, ultrasound images, where intensity
values represent acoustic impedance differences between boundaries, may also
benefit from the boundary delineation approach. Our method uses reinforcement
learning to train a controller function to localise boundaries of ROIs using a
reward derived from a pre-trained boundary-presence classifier. The classifier
indicates when an object boundary is encountered within a patch, as the
controller modifies the patch location in a sequential Markov decision process.
The classifier itself is trained using only binary patch-level labels of object
presence, which are the only labels used during training of the entire boundary
delineation framework, and serves as a weak signal to inform the boundary
delineation. The use of a controller function ensures that a sliding window
over the entire image is not necessary. It also prevents possible
false-positive or -negative cases by minimising number of patches passed to the
boundary-presence classifier. We evaluate our proposed approach for a
clinically relevant task of prostate gland segmentation on trans-rectal
ultrasound images. We show improved performance compared to other tested weakly
supervised methods, using the same labels e.g., multiple instance learning.Comment: Accepted to MICCAI Workshop MLMI 2023 (14th International Conference
on Machine Learning in Medical Imaging
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
Image Processing and Analysis for Preclinical and Clinical Applications
Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis
Artificial Intelligence in Radiation Therapy
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy
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