30 research outputs found
SMILE-UHURA Challenge - Small vessel segmentation at mesoscopic scale from ultra-high resolution 7T magnetic resonance angiograms
No full textDiagnosis of cardiovascular diseases using interpretable cardiac magnetic resonance-derived latent factors
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Machine learning approach for segmenting glands in colon histology images using local intensity and texture features
No full textsoumickmj/DenseInferenceWrapper: Initial release
No full textA python wrapper for Krähenbühls dense CRF for medical image volumes
DDoS-UNet: Incorporating Temporal Information Using Dynamic Dual-Channel UNet for Enhancing Super-Resolution of Dynamic MRI
No full textMagnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for interventions to visualise movements or changes of the target organ. However, such MRI acquisitions with high temporal resolution suffer from limited spatial resolution - also known as the spatio-temporal trade-off of dynamic MRI. Several approaches, including deep learning based super-resolution approaches by treating each timepoint as individual volumes. This research addresses this issue by creating a deep learning model which attempts to learn both spatial and temporal relationships. A modified 3D UNet model, DDoS-UNet, is proposed - which takes the low-resolution volume of the current timepoint along with a prior image volume. Initially, the network is supplied with a static high-resolution planning scan as the prior image along with the low-resolution input to super-resolve the first timepoint. Then it continues step-wise by using the super-resolved timepoints as the prior image while super-resolving the subsequent timepoints. The model performance was tested with 3D dynamic data that was undersampled to different in-plane levels and achieved an average SSIM value of while reconstructing only 4% of the k-space - which could result in a theoretical acceleration factor of 25. The proposed approach can be used to reduce the required scan-time while achieving high spatial resolution - consequently alleviating the spatio-temporal trade-off of dynamic MRI, by incorporating prior knowledge of spatio-temporal information from the available high-resolution planning scan and the existing temporal redundancy of time-series images into the network model
Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
Full text linkDeep learning models have shown their potential for several applications.
However, most of the models are opaque and difficult to trust due to their
complex reasoning - commonly known as the black-box problem. Some fields, such
as medicine, require a high degree of transparency to accept and adopt such
technologies. Consequently, creating explainable/interpretable models or
applying post-hoc methods on classifiers to build trust in deep learning models
are required. Moreover, deep learning methods can be used for segmentation
tasks, which typically require hard-to-obtain, time-consuming
manually-annotated segmentation labels for training. This paper introduces
three inherently-explainable classifiers to tackle both of these problems as
one. The localisation heatmaps provided by the networks -- representing the
models' focus areas and being used in classification decision-making -- can be
directly interpreted, without requiring any post-hoc methods to derive
information for model explanation. The models are trained by using the input
image and only the classification labels as ground-truth in a supervised
fashion - without using any information about the location of the region of
interest (i.e. the segmentation labels), making the segmentation training of
the models weakly-supervised through classification labels. The final
segmentation is obtained by thresholding these heatmaps. The models were
employed for the task of multi-class brain tumour classification using two
different datasets, resulting in the best F1-score of 0.93 for the supervised
classification task while securing a median Dice score of 0.670.08 for the
weakly-supervised segmentation task. Furthermore, the obtained accuracy on a
subset of tumour-only images outperformed the state-of-the-art glioma tumour
grading binary classifiers with the best model achieving 98.7\% accuracy
Upgraded W-Net with Attention Gates and Its Application in Unsupervised 3D Liver Segmentation
No full textClassification of brain tumours in MR images using deep spatiospatial models
No full textA brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning, and magnetic resonance imaging is the principal imaging modality for diagnosing brain tumours and their extent. Deep Learning methods in computer vision applications have shown significant improvement in recent years, most of which can be credited to the fact that a sizeable amount of data is available to train models, and the improvements in the model architectures yield better approximations in a supervised setting. Classifying tumours using such deep learning methods has made significant progress with the availability of open datasets with reliable annotations. Typically those methods are either 3D models, which use 3D volumetric MRIs or even 2D models considering each slice separately. However, by treating one spatial dimension separately or by considering the slices as a sequence of images over time, spatiotemporal models can be employed as "spatiospatial" models for this task. These models have the capabilities of learning specific spatial and temporal relationships while reducing computational costs. This paper uses two spatiotemporal models, ResNet (2+1)D and ResNet Mixed Convolution, to classify different types of brain tumours. It was observed that both these models performed superior to the pure 3D convolutional model, ResNet18. Furthermore, it was also observed that pre-training the models on a different, even unrelated dataset before training them for the task of tumour classification improves the performance. Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0.9345 and a test accuracy of 96.98%, while at the same time being the model with the least computational cost
Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks
Full text linkAdditive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all manufactured samples of a batch, X-ray computed tomography (X-CT) is often used combined with automated anomaly detection. For the latter, deep learning (DL) anomaly detection techniques are increasingly, as they can be trained to be robust to the material being analysed and resilient towards poor image quality. Unfortunately, most recent and popular DL models have been developed for 2D image processing, thereby disregarding valuable volumetric information.
This study revisits recent supervised (UNet, UNet++, UNet 3+, MSS-UNet) and unsupervised (VAE, ceVAE, gmVAE, vqVAE) DL models for porosity analysis of AM samples from X-CT images and extends them to accept 3D input data with a 3D-patch pipeline for lower computational requirements, improved efficiency and generalisability. The supervised models were trained using the Focal Tversky loss to address class imbalance that arises from the low porosity in the training datasets. The output of the unsupervised models is post-processed to reduce misclassifications caused by their inability to adequately represent the object surface. The findings were cross-validated in a 5-fold fashion and include: a performance benchmark of the DL models, an evaluation of the post-processing algorithm, an evaluation of the effect of training supervised models with the output of unsupervised models. In a final performance benchmark on a test set with poor image quality, the best performing supervised model was UNet++ with an average precision of 0.751 0.030, while the best unsupervised model was the post-processed ceVAE with 0.830 0.003. The VAE/ceVAE models demonstrated superior capabilities, particularly when leveraging post-processing techniques
