7 research outputs found
BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRI
The study presents a deep learning framework aimed at synthesizing 3D MRI
volumes from three-dimensional ultrasound images of the brain utilizing the
Pix2Pix GAN model. The process involves inputting a 3D volume of ultrasounds
into a UNET generator and patch discriminator, generating a corresponding 3D
volume of MRI. Model performance was evaluated using losses on the
discriminator and generator applied to a dataset of 3D ultrasound and MRI
images. The results indicate that the synthesized MRI images exhibit some
similarity to the expected outcomes. Despite challenges related to dataset
size, computational resources, and technical complexities, the method
successfully generated MRI volume with a satisfactory similarity score meant to
serve as a baseline for further research. It underscores the potential of deep
learning-based volume synthesis techniques for ultrasound to MRI conversion,
showcasing their viability for medical applications. Further refinement and
exploration are warranted for enhanced clinical relevance.Comment: 6 page
Learning to Segment Microscopy Images with Lazy Labels
The need for labour intensive pixel-wise annotation is a major limitation of
many fully supervised learning methods for segmenting bioimages that can
contain numerous object instances with thin separations. In this paper, we
introduce a deep convolutional neural network for microscopy image
segmentation. Annotation issues are circumvented by letting the network being
trainable on coarse labels combined with only a very small number of images
with pixel-wise annotations. We call this new labelling strategy `lazy' labels.
Image segmentation is stratified into three connected tasks: rough inner region
detection, object separation and pixel-wise segmentation. These tasks are
learned in an end-to-end multi-task learning framework. The method is
demonstrated on two microscopy datasets, where we show that the model gives
accurate segmentation results even if exact boundary labels are missing for a
majority of annotated data. It brings more flexibility and efficiency for
training deep neural networks that are data hungry and is applicable to
biomedical images with poor contrast at the object boundaries or with diverse
textures and repeated patterns
DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation
Deep learning architecture with convolutional neural network achieves outstanding success in the field of computer vision. Where U-Net has made a great breakthrough in biomedical image segmentation and has been widely applied in a wide range of practical scenarios. However, the equal design of every downsampling layer in the encoder part and simply stacked convolutions do not allow U-Net to extract sufficient information of features from different depths. The increasing complexity of medical images brings new challenges to the existing methods. In this paper, we propose a deeper and more compact split-attention u-shape network, which efficiently utilises low-level and high-level semantic information based on two frameworks: primary feature conservation and compact split-attention block. We evaluate the proposed model on CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018, SegPC-2021 and BraTS-2021 datasets. As a result, our proposed model displays better performance than other state-of-the-art methods in terms of the mean intersection over union and dice coefficient. More significantly, the proposed model demonstrates excellent segmentation performance on challenging images. The code for our work and more technical details can be found at https://github.com/xq141839/DCSAU-Net
Deep Semantic Segmentation of Natural and Medical Images: A Review
The semantic image segmentation task consists of classifying each pixel of an
image into an instance, where each instance corresponds to a class. This task
is a part of the concept of scene understanding or better explaining the global
context of an image. In the medical image analysis domain, image segmentation
can be used for image-guided interventions, radiotherapy, or improved
radiological diagnostics. In this review, we categorize the leading deep
learning-based medical and non-medical image segmentation solutions into six
main groups of deep architectural, data synthesis-based, loss function-based,
sequenced models, weakly supervised, and multi-task methods and provide a
comprehensive review of the contributions in each of these groups. Further, for
each group, we analyze each variant of these groups and discuss the limitations
of the current approaches and present potential future research directions for
semantic image segmentation.Comment: 45 pages, 16 figures. Accepted for publication in Springer Artificial
Intelligence Revie
Machine Learning Models to automate Radiotherapy Structure Name Standardization
Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and `Other\u27 organs for monitoring present and future medications. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and `Other\u27 organs is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. We compare both traditional methods and deep neural network-based approaches on the multimodal vision-language prostate cancer patient data, compiled from the radiotherapy centers of the US Veterans Health Administration (VHA) and Virginia Commonwealth University (VCU) for structure name standardization. These de-identified data comprise 16,290 prostate structures. Our method integrates the multimodal textual and imaging data with Convolutional Neural Network (CNN)-based deep learning approaches such as CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet) and shows improved results in prostate radiotherapy structure name standardization. Our proposed deep neural network-based approach on the multimodal vision-language prostate cancer patient data provides state-of-the-art results for structure name standardization. Evaluation with macro-averaged F1 score shows that our CNN model with single-modal textual data usually performs better than previous studies. We also experimented with various combinations of multimodal data (masked images, masked dose) besides textual data. The models perform well on textual data alone, while the addition of imaging data shows that deep neural networks achieve better performance using information present in other modalities. Our pipeline can successfully standardize the Organs-at-Risk and the Planning Target Volumes, which are of utmost interest to the clinicians and simultaneously, performs very well on the `Other\u27 organs. We performed comprehensive experiments by varying input data modalities to show that using masked images and masked dose data with text outperforms the combination of other input modalities. We also undersampled the majority class, i.e., the `Other\u27 class, at different degrees and conducted extensive experiments to demonstrate that a small amount of majority class undersampling is essential for superior performance. Overall, our proposed integrated, deep neural network-based architecture for prostate structure name standardization can solve several challenges associated with multimodal data. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and presents the state-of-the-art in this domain