Point Projection Mapping System for Tracking, Registering, Labeling and
Validating Optical Tissue Measurements
- URL: http://arxiv.org/abs/2311.13378v1
- Date: Wed, 22 Nov 2023 13:19:41 GMT
- Title: Point Projection Mapping System for Tracking, Registering, Labeling and
Validating Optical Tissue Measurements
- Authors: Lianne Feenstra, Stefan D.van der Stel, Marcos Da Silva Guimaraes,
Theo J.M Ruers and Behdad Dashtbozorg
- Abstract summary: This paper introduces a newly developed Point Projection Mapping system, which allows non-destructive tracking of the measurement locations on tissue specimens.
A framework for accurate registration, validation, and labeling with histopathology results is proposed and validated on a case study.
- Score: 0.1874930567916036
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Validation of newly developed optical tissue sensing techniques for tumor
detection during cancer surgery requires an accurate correlation with
histological results. Additionally, such accurate correlation facilitates
precise data labeling for developing high-performance machine-learning tissue
classification models. In this paper, a newly developed Point Projection
Mapping system will be introduced, which allows non-destructive tracking of the
measurement locations on tissue specimens. Additionally, a framework for
accurate registration, validation, and labeling with histopathology results is
proposed and validated on a case study. The proposed framework provides a more
robust and accurate method for tracking and validation of optical tissue
sensing techniques, which saves time and resources compared to conventional
techniques available.
Related papers
- TopOC: Topological Deep Learning for Ovarian and Breast Cancer Diagnosis [3.262230127283452]
Topological data analysis offers a unique approach by extracting essential information through the evaluation of topological patterns across different color channels.
We show that the inclusion of topological features significantly improves the differentiation of tumor types in ovarian and breast cancers.
arXiv Detail & Related papers (2024-10-13T12:24:13Z) - Fully Automated OCT-based Tissue Screening System [5.646346784449182]
The system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples.
This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery.
arXiv Detail & Related papers (2024-05-15T14:56:17Z) - SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery [39.66047935237083]
We use statistical analysis of speckle patterns to incorporate statistical pathology-specific prior knowledge.
Our findings indicate statistically different speckle patterns within the retina and between retinal layers and surgical tools.
The proposed segmentation model aims to refine the statistical findings based on prior tissue understanding.
arXiv Detail & Related papers (2024-04-30T11:49:29Z) - CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images.
The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism.
We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
arXiv Detail & Related papers (2024-03-21T15:13:36Z) - Real-time landmark detection for precise endoscopic submucosal
dissection via shape-aware relation network [51.44506007844284]
We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery.
We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks.
We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
arXiv Detail & Related papers (2021-11-08T07:57:30Z) - Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based
Sparse PCA Network [93.22587316229954]
We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E)
We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
arXiv Detail & Related papers (2021-10-27T19:28:36Z) - A parameter refinement method for Ptychography based on Deep Learning
concepts [55.41644538483948]
coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability.
A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction.
We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
arXiv Detail & Related papers (2021-05-18T10:15:17Z) - Reciprocal Landmark Detection and Tracking with Extremely Few
Annotations [10.115679843920958]
We propose a new end-to-end reciprocal detection and tracking model to handle the sparse nature of echocardiography labels.
The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks.
arXiv Detail & Related papers (2021-01-27T06:59:41Z) - Tattoo tomography: Freehand 3D photoacoustic image reconstruction with
an optical pattern [49.240017254888336]
Photoacoustic tomography (PAT) is a novel imaging technique that can resolve both morphological and functional tissue properties.
A current drawback is the limited field-of-view provided by the conventionally applied 2D probes.
We present a novel approach to 3D reconstruction of PAT data that does not require an external tracking system.
arXiv Detail & Related papers (2020-11-10T09:27:56Z) - Mitosis Detection Under Limited Annotation: A Joint Learning Approach [5.117836409118142]
Deep learning-based mitotic detection is on par with pathologists, but it requires large labeled data for training.
We propose a deep classification framework for enhancing mitosis detection by leveraging class label information, via softmax loss, and spatial distribution information among samples, via distance metric learning.
Our framework significantly improves the detection with small training data and achieves on par or superior performance compared to state-of-the-art methods for using the entire training data.
arXiv Detail & Related papers (2020-06-17T10:46:29Z) - ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed
Quality Labeling Using Neural Networks [69.25956542388653]
Deep learning (DL) algorithms are gaining weight in academic and industrial settings.
We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework.
The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
arXiv Detail & Related papers (2020-05-11T16:29:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.