570 research outputs found
Self-Supervised Learning for Spinal MRIs
A significant proportion of patients scanned in a clinical setting have
follow-up scans. We show in this work that such longitudinal scans alone can be
used as a form of 'free' self-supervision for training a deep network. We
demonstrate this self-supervised learning for the case of T2-weighted sagittal
lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network
(CNN) is trained using two losses: (i) a contrastive loss on whether the scan
is of the same person (i.e. longitudinal) or not, together with (ii) a
classification loss on predicting the level of vertebral bodies. The
performance of this pre-trained network is then assessed on a grading
classification task. We experiment on a dataset of 1016 subjects, 423
possessing follow-up scans, with the end goal of learning the disc degeneration
radiological gradings attached to the intervertebral discs. We show that the
performance of the pre-trained CNN on the supervised classification task is (i)
superior to that of a network trained from scratch; and (ii) requires far fewer
annotated training samples to reach an equivalent performance to that of the
network trained from scratch.Comment: 3rd Workshop on Deep Learning in Medical Image Analysi
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net
Learning-based fully automated prediction of lumbar disc degeneration progression with specified clinical parameters and preliminary validation
Background:
Lumbar disc degeneration (LDD) may be related to aging, biomechanical and genetic factors. Despite the extensive work on understanding its etiology, there is currently no automated tool for accurate prediction of its progression. /
Purpose:
We aim to establish a novel deep learning-based pipeline to predict the progression of LDD-related findings using lumbar MRIs. /
Materials and methods:
We utilized our dataset with MRIs acquired from 1,343 individual participants (taken at the baseline and the 5-year follow-up timepoint), and progression assessments (the Schneiderman score, disc bulging, and Pfirrmann grading) that were labelled by spine specialists with over ten years clinical experience. Our new pipeline was realized by integrating the MRI-SegFlow and the Visual Geometry Group-Medium (VGG-M) for automated disc region detection and LDD progression prediction correspondingly. The LDD progression was quantified by comparing the Schneiderman score, disc bulging and Pfirrmann grading at the baseline and at follow-up. A fivefold cross-validation was conducted to assess the predictive performance of the new pipeline. /
Results:
Our pipeline achieved very good performances on the LDD progression prediction, with high progression prediction accuracy of the Schneiderman score (Accuracy: 90.2 ± 0.9%), disc bulging (Accuracy: 90.4% ± 1.1%), and Pfirrmann grading (Accuracy: 89.9% ± 2.1%). /
Conclusion:
This is the first attempt of using deep learning to predict LDD progression on a large dataset with 5-year follow-up. Requiring no human interference, our pipeline can potentially achieve similar predictive performances in new settings with minimal efforts
Denoising diffusion-based MR to CT image translation enables whole spine vertebral segmentation in 2D and 3D without manual annotations
Background: Automated segmentation of spinal MR images plays a vital role
both scientifically and clinically. However, accurately delineating posterior
spine structures presents challenges.
Methods: This retrospective study, approved by the ethical committee,
involved translating T1w and T2w MR image series into CT images in a total of
n=263 pairs of CT/MR series. Landmark-based registration was performed to align
image pairs. We compared 2D paired (Pix2Pix, denoising diffusion implicit
models (DDIM) image mode, DDIM noise mode) and unpaired (contrastive unpaired
translation, SynDiff) image-to-image translation using "peak signal to noise
ratio" (PSNR) as quality measure. A publicly available segmentation network
segmented the synthesized CT datasets, and Dice scores were evaluated on
in-house test sets and the "MRSpineSeg Challenge" volumes. The 2D findings were
extended to 3D Pix2Pix and DDIM.
Results: 2D paired methods and SynDiff exhibited similar translation
performance and Dice scores on paired data. DDIM image mode achieved the
highest image quality. SynDiff, Pix2Pix, and DDIM image mode demonstrated
similar Dice scores (0.77). For craniocaudal axis rotations, at least two
landmarks per vertebra were required for registration. The 3D translation
outperformed the 2D approach, resulting in improved Dice scores (0.80) and
anatomically accurate segmentations in a higher resolution than the original MR
image.
Conclusion: Two landmarks per vertebra registration enabled paired
image-to-image translation from MR to CT and outperformed all unpaired
approaches. The 3D techniques provided anatomically correct segmentations,
avoiding underprediction of small structures like the spinous process.Comment: 35 pages, 7 figures, Code and a model weights available
https://doi.org/10.5281/zenodo.8221159 and
https://doi.org/10.5281/zenodo.819869
Using Deep Learning to Analyze Materials in Medical Images
Modern deep learning architectures have become increasingly popular in medicine, especially for analyzing medical images. In some medical applications, deep learning image analysis models have been more accurate at predicting medical conditions than experts. Deep learning has also been effective for material analysis on photographs. We aim to leverage deep learning to perform material analysis on medical images. Because material datasets for medicine are scarce, we first introduce a texture dataset generation algorithm that automatically samples desired textures from annotated or unannotated medical images. Second, we use a novel Siamese neural network called D-CNN to predict patch similarity and build a distance metric between medical materials. Third, we apply and update a material analysis network from prior research, called MMAC-CNN, to predict materials in texture samples while also learning attributes that further separate the material space. In our experiments, we found that the MMAC-CNN is 89.5% accurate at predicting materials in texture patches, while also transferring knowledge of materials between image modalities
Self-supervised learning methods for label-efficient dental caries classification
High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three selfsupervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce ě45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive
Trustworthy Deep Learning for Medical Image Segmentation
Despite the recent success of deep learning methods at achieving new
state-of-the-art accuracy for medical image segmentation, some major
limitations are still restricting their deployment into clinics. One major
limitation of deep learning-based segmentation methods is their lack of
robustness to variability in the image acquisition protocol and in the imaged
anatomy that were not represented or were underrepresented in the training
dataset. This suggests adding new manually segmented images to the training
dataset to better cover the image variability. However, in most cases, the
manual segmentation of medical images requires highly skilled raters and is
time-consuming, making this solution prohibitively expensive. Even when
manually segmented images from different sources are available, they are rarely
annotated for exactly the same regions of interest. This poses an additional
challenge for current state-of-the-art deep learning segmentation methods that
rely on supervised learning and therefore require all the regions of interest
to be segmented for all the images to be used for training. This thesis
introduces new mathematical and optimization methods to mitigate those
limitations.Comment: PhD thesis successfully defended on 1st July 2022. Examiners: Prof
Sotirios Tsaftaris and Dr Wenjia Ba
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