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Found 51 papers, 14 papers with code
Self-distillation for surgical action recognition
Surgical scene understanding is a key prerequisite for contextaware decision support in the operating room.
Deployment of Image Analysis Algorithms under Prevalence Shifts
Domain gaps are among the most relevant roadblocks in the clinical translation of machine learning (ML)-based solutions for medical image analysis.
Semantic segmentation of surgical hyperspectral images under geometric domain shifts
According to a comprehensive validation on six different OOD data sets comprising 600 RGB and hyperspectral imaging (HSI) cubes from 33 pigs semantically annotated with 19 classes, we demonstrate a large performance drop of SOA organ segmentation networks applied to geometric OOD data.
Unsupervised Domain Transfer with Conditional Invertible Neural Networks
Synthetic medical image generation has evolved as a key technique for neural network training and validation.
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge
Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science.
Sources of performance variability in deep learning-based polyp detection
We address this gap in the literature in the context of colon cancer screening.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
Labeling instructions matter in biomedical image analysis
Here, we present the first systematic study of labeling instructions and their impact on annotation quality in the field.
Metrics reloaded: Pitfalls and recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Ten years of image analysis and machine learning competitions in dementia
Key for increasing impact in this way are larger testing data sizes, which could be reached by sharing algorithms rather than data to exploit data that cannot be shared.
Robust deep learning-based semantic organ segmentation in hyperspectral images
To address this gap in the literature, we are investigating the following research questions based on hyperspectral imaging (HSI) data of pigs acquired in an open surgery setting: (1) What is an adequate representation of HSI data for neural network-based fully automated organ segmentation, especially with respect to the spatial granularity of the data (pixels vs. superpixels vs. patches vs. full images)?
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems.
Ranked #1 on
Surgical phase recognition
on HeiChole Benchmark
Task Fingerprinting for Meta Learning in Biomedical Image Analysis
Task fingerprinting could thus become an important tool for meta learning in SDS and other fields of biomedical image analysis.
How can we learn (more) from challenges? A statistical approach to driving future algorithm development
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner.
Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis
Sepsis is a leading cause of mortality and critical illness worldwide.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Video-rate multispectral imaging in laparoscopic surgery: First-in-human application
Multispectral and hyperspectral imaging (MSI/HSI) can provide clinically relevant information on morphological and functional tissue properties.
Semantic segmentation of multispectral photoacoustic images using deep learning
Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Photoacoustic image synthesis with generative adversarial networks
Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution.
Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge
Surgical data science is revolutionizing minimally invasive surgery by enabling context-aware applications.
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge
The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China.
SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction
The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths with coverage over the majority of the endoscopic images.
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks
One example is the registration of 2D X-ray images with preoperative three-dimensional computed tomography (CT) images in intraoperative surgical guidance systems.
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Multispectral photoacoustic imaging (PAI) is an emerging imaging modality which enables the recovery of functional tissue parameters such as blood oxygenation.
Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern
Methods: In this paper, we present a novel approach to 3D reconstruction of PAT data (Tattoo tomography) that does not require an external tracking system and can smoothly be integrated into clinical workflows.
Deep learning for biomedical photoacoustic imaging: A review
Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications.
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.
Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
Image-based tracking of medical instruments is an integral part of surgical data science applications.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
Out of distribution detection for intra-operative functional imaging
Multispectral optical imaging is becoming a key tool in the operating room.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Methods and open-source toolkit for analyzing and visualizing challenge results
Biomedical challenges have become the de facto standard for benchmarking biomedical image analysis algorithms.
BIAS: Transparent reporting of biomedical image analysis challenges
The number of biomedical image analysis challenges organized per year is steadily increasing.
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images.
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
Assessment of the specific hardware used in conjunction with such algorithms, however, has not properly addressed the possibility that the problem may be ill-posed.
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
2017 Robotic Instrument Segmentation Challenge
In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison.
Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)
In this work, we tackle the challenge by employing learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI) to obtain quantitative estimates for blood oxygenation.
Analyzing Inverse Problems with Invertible Neural Networks
Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of parameters.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
The paper presents a comparative validation study of different vision-based methods for instrument segmentation and tracking in the context of robotic as well as conventional laparoscopic surgery.
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
In this paper, we propose the re-colorization of medical images with a generative adversarial network (GAN)-based architecture as auxiliary task.
Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging
Intra-operative measurements of tissue shape and multi/ hyperspectral information have the potential to provide surgical guidance and decision making support.
Context encoding enables machine learning-based quantitative photoacoustics
Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and interventional therapy of various diseases.
Clickstream analysis for crowd-based object segmentation with confidence
Using a total of 29, 000 crowd annotations performed on publicly available data of different object classes, we show that (1) our method is highly accurate in estimating the segmentation quality based on clickstream data, (2) outperforms state-of-the-art methods for merging multiple annotations.
The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms
We show that the RMS is of limited expressiveness for algorithm selection and introduce the HCI Stereo Metrics.
