13 research outputs found
Generative Adversarial Networks based Skin Lesion Segmentation
Skin cancer is a serious condition that requires accurate identification and
treatment. One way to assist clinicians in this task is by using computer-aided
diagnosis (CAD) tools that can automatically segment skin lesions from
dermoscopic images. To this end, a new adversarial learning-based framework
called EGAN has been developed. This framework uses an unsupervised generative
network to generate accurate lesion masks. It consists of a generator module
with a top-down squeeze excitation-based compound scaled path and an asymmetric
lateral connection-based bottom-up path, and a discriminator module that
distinguishes between original and synthetic masks. Additionally, a
morphology-based smoothing loss is implemented to encourage the network to
create smooth semantic boundaries of lesions. The framework is evaluated on the
International Skin Imaging Collaboration (ISIC) Lesion Dataset 2018 and
outperforms the current state-of-the-art skin lesion segmentation approaches
with a Dice coefficient, Jaccard similarity, and Accuracy of 90.1%, 83.6%, and
94.5%, respectively. This represents a 2% increase in Dice Coefficient, 1%
increase in Jaccard Index, and 1% increase in Accuracy
Framing image registration as a landmark detection problem for better representation of clinical relevance
Nowadays, registration methods are typically evaluated based on
sub-resolution tracking error differences. In an effort to reinfuse this
evaluation process with clinical relevance, we propose to reframe image
registration as a landmark detection problem. Ideally, landmark-specific
detection thresholds are derived from an inter-rater analysis. To approximate
this costly process, we propose to compute hit rate curves based on the
distribution of errors of a sub-sample inter-rater analysis. Therefore, we
suggest deriving thresholds from the error distribution using the formula:
median + delta * median absolute deviation. The method promises differentiation
of previously indistinguishable registration algorithms and further enables
assessing the clinical significance in algorithm development
The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients
Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes, and still an unsolved problem. This paper describes the first Brain Tumor Sequence Registration (BraTS-Reg) challenge, focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a brain diffuse glioma. The BraTS-Reg challenge intends to establish a public benchmark environment for deformable registration algorithms. The associated dataset comprises de-identified multi-institutional multi-parametric MRI (mpMRI) data, curated for each scan's size and resolution, according to a common anatomical template. Clinical experts have generated extensive annotations of landmarks points within the scans, descriptive of distinct anatomical locations across the temporal domain. The training data along with these ground truth annotations will be released to participants to design and develop their registration algorithms, whereas the annotations for the validation and the testing data will be withheld by the organizers and used to evaluate the containerized algorithms of the participants. Each submitted algorithm will be quantitatively evaluated using several metrics, such as the Median Absolute Error (MAE), Robustness, and the Jacobian determinant
GaNDLF:A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Deep Learning (DL) has greatly highlighted the potential impact of optimized machine learning in both the scientific and clinical communities. The advent of open-source DL libraries from major industrial entities, such as TensorFlow (Google), PyTorch (Facebook), and MXNet (Apache), further contributes to DL promises on the democratization of computational analytics. However, increased technical and specialized background is required to develop DL algorithms, and the variability of implementation details hinders their reproducibility. Towards lowering the barrier and making the mechanism of DL development, training, and inference more stable, reproducible, and scalable, without requiring an extensive technical background, this manuscript proposes the Generally Nuanced Deep Learning Framework (GaNDLF). With built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes, and multi-GPU training, as well as the ability to work with both radiographic and histologic imaging, GaNDLF aims to provide an end-to-end solution for all DL-related tasks, to tackle problems in medical imaging and provide a robust application framework for deployment in clinical workflows
MoNuSAC2020:A Multi-Organ Nuclei Segmentation and Classification Challenge
Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public
The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights
Standardizing convolutional filters that enhance specific structures and patterns in medical imaging enables reproducible radiomics analyses, improving consistency and reliability for enhanced clinical insights.
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking
Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic Relevance
Glioblastoma is the most common and aggressive malignant adult tumor of the
central nervous system, with a grim prognosis and heterogeneous morphologic and
molecular profiles. Since adopting the current standard-of-care treatment 18
years ago, no substantial prognostic improvement has been noticed. Accurate
prediction of patient overall survival (OS) from histopathology whole slide
images (WSI) integrated with clinical data using advanced computational methods
could optimize clinical decision-making and patient management. Here, we focus
on identifying prognostically relevant glioblastoma characteristics from H&E
stained WSI & clinical data relating to OS. The exact approach for WSI
capitalizes on the comprehensive curation of apparent artifactual content and
an interpretability mechanism via a weakly supervised attention-based
multiple-instance learning algorithm that further utilizes clustering to
constrain the search space. The automatically placed patterns of high
diagnostic value classify each WSI as representative of short or
long-survivors. Further assessment of the prognostic relevance of the
associated clinical patient data is performed both in isolation and in an
integrated manner, using XGBoost and SHapley Additive exPlanations (SHAP).
Identifying tumor morphological & clinical patterns associated with short and
long OS will enable the clinical neuropathologist to provide additional
relevant prognostic information to the treating team and suggest avenues of
biological investigation for understanding and potentially treating
glioblastoma
Generative adversarial networks based skin lesion segmentation
Abstract Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way to assist clinicians in this task is using computer-aided diagnosis tools that automatically segment skin lesions from dermoscopic images. We propose a novel adversarial learning-based framework called Efficient-GAN (EGAN) that uses an unsupervised generative network to generate accurate lesion masks. It consists of a generator module with a top-down squeeze excitation-based compound scaled path, an asymmetric lateral connection-based bottom-up path, and a discriminator module that distinguishes between original and synthetic masks. A morphology-based smoothing loss is also implemented to encourage the network to create smooth semantic boundaries of lesions. The framework is evaluated on the International Skin Imaging Collaboration Lesion Dataset. It outperforms the current state-of-the-art skin lesion segmentation approaches with a Dice coefficient, Jaccard similarity, and accuracy of 90.1%, 83.6%, and 94.5%, respectively. We also design a lightweight segmentation framework called Mobile-GAN (MGAN) that achieves comparable performance as EGAN but with an order of magnitude lower number of training parameters, thus resulting in faster inference times for low compute resource settings