6 research outputs found
SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT
The Segment Anything Model (SAM) has gained significant attention in the
field of image segmentation due to its impressive capabilities and prompt-based
interface. While SAM has already been extensively evaluated in various domains,
its adaptation to retinal OCT scans remains unexplored. To bridge this research
gap, we conduct a comprehensive evaluation of SAM and its adaptations on a
large-scale public dataset of OCTs from RETOUCH challenge. Our evaluation
covers diverse retinal diseases, fluid compartments, and device vendors,
comparing SAM against state-of-the-art retinal fluid segmentation methods.
Through our analysis, we showcase adapted SAM's efficacy as a powerful
segmentation model in retinal OCT scans, although still lagging behind
established methods in some circumstances. The findings highlight SAM's
adaptability and robustness, showcasing its utility as a valuable tool in
retinal OCT image analysis and paving the way for further advancements in this
domain
Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data
Robust forecasting of the future anatomical changes inflicted by an ongoing
disease is an extremely challenging task that is out of grasp even for
experienced healthcare professionals. Such a capability, however, is of great
importance since it can improve patient management by providing information on
the speed of disease progression already at the admission stage, or it can
enrich the clinical trials with fast progressors and avoid the need for control
arms by the means of digital twins. In this work, we develop a deep learning
method that models the evolution of age-related disease by processing a single
medical scan and providing a segmentation of the target anatomy at a requested
future point in time. Our method represents a time-invariant physical process
and solves a large-scale problem of modeling temporal pixel-level changes
utilizing NeuralODEs. In addition, we demonstrate the approaches to incorporate
the prior domain-specific constraints into our method and define temporal Dice
loss for learning temporal objectives. To evaluate the applicability of our
approach across different age-related diseases and imaging modalities, we
developed and tested the proposed method on the datasets with 967 retinal OCT
volumes of 100 patients with Geographic Atrophy, and 2823 brain MRI volumes of
633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposed
method outperformed the related baseline models in the atrophy growth
prediction. For Alzheimer's Disease, the proposed method demonstrated
remarkable performance in predicting the brain ventricle changes induced by the
disease, achieving the state-of-the-art result on TADPOLE challenge
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
Deep learning has become a valuable tool for the automation of certain
medical image segmentation tasks, significantly relieving the workload of
medical specialists. Some of these tasks require segmentation to be performed
on a subset of the input dimensions, the most common case being 3D-to-2D.
However, the performance of existing methods is strongly conditioned by the
amount of labeled data available, as there is currently no data efficient
method, e.g. transfer learning, that has been validated on these tasks. In this
work, we propose a novel convolutional neural network (CNN) and self-supervised
learning (SSL) method for label-efficient 3D-to-2D segmentation. The CNN is
composed of a 3D encoder and a 2D decoder connected by novel 3D-to-2D blocks.
The SSL method consists of reconstructing image pairs of modalities with
different dimensionality. The approach has been validated in two tasks with
clinical relevance: the en-face segmentation of geographic atrophy and
reticular pseudodrusen in optical coherence tomography. Results on different
datasets demonstrate that the proposed CNN significantly improves the state of
the art in scenarios with limited labeled data by up to 8% in Dice score.
Moreover, the proposed SSL method allows further improvement of this
performance by up to 23%, and we show that the SSL is beneficial regardless of
the network architecture.Comment: To appear in MICCAI 2023. Code:
https://github.com/j-morano/multimodal-ssl-fp
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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-phenotype, as their sub-regions are depicted by
varying intensity profiles disseminated across multi-parametric magnetic
resonance imaging (mpMRI) scans, reflecting varying biological properties.
Their heterogeneous shape, extent, and location are some of the factors that
make these tumors difficult to resect, and in some cases inoperable. The amount
of resected tumor is a factor also considered in longitudinal scans, when
evaluating the apparent tumor for potential diagnosis of progression.
Furthermore, there is mounting evidence that accurate segmentation of the
various tumor sub-regions can offer the basis for quantitative image analysis
towards prediction of patient overall survival. This study assesses the
state-of-the-art machine learning (ML) methods used for brain tumor image
analysis in mpMRI scans, during the last seven instances of the International
Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we
focus on i) evaluating segmentations of the various glioma sub-regions in
pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue
of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO
criteria, and iii) predicting the overall survival from pre-operative mpMRI
scans of patients that underwent gross total resection. Finally, we investigate
the challenge of identifying the best ML algorithms for each of these tasks,
considering that apart from being diverse on each instance of the challenge,
the multi-institutional mpMRI BraTS dataset has also been a continuously
evolving/growing dataset
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-phenotype, as their sub-regions are depicted by
varying intensity profiles disseminated across multi-parametric magnetic
resonance imaging (mpMRI) scans, reflecting varying biological properties.
Their heterogeneous shape, extent, and location are some of the factors that
make these tumors difficult to resect, and in some cases inoperable. The amount
of resected tumor is a factor also considered in longitudinal scans, when
evaluating the apparent tumor for potential diagnosis of progression.
Furthermore, there is mounting evidence that accurate segmentation of the
various tumor sub-regions can offer the basis for quantitative image analysis
towards prediction of patient overall survival. This study assesses the
state-of-the-art machine learning (ML) methods used for brain tumor image
analysis in mpMRI scans, during the last seven instances of the International
Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we
focus on i) evaluating segmentations of the various glioma sub-regions in
pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue
of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO
criteria, and iii) predicting the overall survival from pre-operative mpMRI
scans of patients that underwent gross total resection. Finally, we investigate
the challenge of identifying the best ML algorithms for each of these tasks,
considering that apart from being diverse on each instance of the challenge,
the multi-institutional mpMRI BraTS dataset has also been a continuously
evolving/growing dataset