Search Results for author:
Found 18 papers, 8 papers with code
AMNCutter: Affinity-Attention-Guided Multi-View Normalized Cutter for Unsupervised Surgical Instrument Segmentation
Recent unsupervised surgical instrument segmentation (USIS) methods primarily rely on pseudo-labels derived from low-level features such as color and optical flow, but these methods show limited effectiveness and generalizability in complex and unseen endoscopic scenarios.
Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning View
In this work, we propose an unsupervised method by reframing the video frame segmentation as a graph partitioning problem and regarding image pixels as graph nodes, which is significantly different from the previous efforts.
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
This highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance.
Rule-based outlier detection of AI-generated anatomy segmentations
There is a dire need for medical imaging datasets with accompanying annotations to perform downstream patient analysis.
Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels
Our approach shows comparable results compared to fully-supervised segmentation methods trained on MR data.
Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects
Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection.
Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery
Our review explores a specific NRR method for registering brain MRI during image-guided neurosurgery and examines various strategies for improving the accuracy and speed of the NRR method.
Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body.
Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations
As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial.
A Registration- and Uncertainty-based Framework for White Matter Tract Segmentation With Only One Annotated Subject
Our method is constructed by proposed registration-based peak augmentation (RPA) and uncertainty-based refining (URe) modules.
The NCI Imaging Data Commons as a platform for reproducible research in computational pathology
Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.
DeepRGVP: A Novel Microstructure-Informed Supervised Contrastive Learning Framework for Automated Identification Of The Retinogeniculate Pathway Using dMRI Tractography
The retinogeniculate pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus.
Slim: interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology
The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations.
Surgical Data Science -- from Concepts toward Clinical Translation
We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Registration of ultrasound volumes based on Euclidean distance transform
Therefore, our aim with this work is to improve the registration of intraoperative US volumes.
Image and Video Processing
Repeatability of Multiparametric Prostate MRI Radiomics Features
In this study we assessed the repeatability of the values of radiomics features for small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI) images.
Large scale digital prostate pathology image analysis combining feature extraction and deep neural network
In this work, we present an analysis pipeline that includes localization of the cancer region, grading, area ratio of different Gleason grades, and cytological/architectural feature extraction.
Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morphometry (VBM) map.
