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1.
Comput Med Imaging Graph ; 88: 101844, 2021 03.
Article in English | MEDLINE | ID: mdl-33477091

ABSTRACT

A multimodal wound image database was created to allow fast development of computer-aided approaches for wound healing monitoring. The developed system with parallel camera optical axes enables multimodal images: photo, thermal, stereo, and depth map of the wound area to be acquired. As a result of using this system a multimodal database of chronic wound images is introduced. It contains 188 image sets of photographs, thermal images, and 3D meshes of the surfaces of chronic wounds acquired during 79 patient visits. Manual wound outlines delineated by an expert are also included in the dataset. All images of each case are additionally coregistered, and both numerical registration parameters and the transformed images are covered in the database. The presented database is publicly available for the research community at https://chronicwounddatabase.eu. That is the first publicly available database for evaluation and comparison of new image-based algorithms in the wound healing monitoring process with coregistered photographs, thermal maps, and 3D models of the wound area. Easily available database of coregistered multimodal data with the raw data set allows faster development of algorithms devoted to wound healing analysis and monitoring.


Subject(s)
Algorithms , Image Processing, Computer-Assisted , Databases, Factual , Humans , Wound Healing
2.
Med Image Anal ; 68: 101898, 2021 02.
Article in English | MEDLINE | ID: mdl-33248330

ABSTRACT

An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the American Association of Physicists in Medicine regulations. Our dose management can operate on incomplete DICOM header metadata by retrieving necessary information from the dose report image by using optical character recognition. For the determination of the patient's effective diameter and water equivalent diameter, a convolutional neural network is employed for the semantic segmentation of the body area in axial CT slices. Validation experiments for the assessment of the SSDE determination and subsequent stages of our methodology involved a total of 335 CT series (60 352 images) from both public databases and our clinical data. We obtained the mean body area segmentation accuracy of 0.9955 and Jaccard index of 0.9752, yielding a slice-wise mean absolute error of effective diameter below 2 mm and water equivalent diameter at 1 mm, both below 1%. Three modes of the SSDE determination approach were investigated and compared to the results provided by the commercial system GE DoseWatch in three different body region categories: head, chest, and abdomen. Statistical analysis was employed to point out some significant remarks, especially in the head category.


Subject(s)
Deep Learning , Humans , Image Processing, Computer-Assisted , Radiation Dosage , Retrospective Studies , Tomography, X-Ray Computed
3.
Comput Med Imaging Graph ; 78: 101664, 2019 12.
Article in English | MEDLINE | ID: mdl-31635911

ABSTRACT

Percutaneous ablation methods are used to treat primary and metastatic liver tumors. Image guided navigation support minimally invasive interventions of rigid anatomical structures. When working with the displacement and deformation of soft tissues during surgery, as in the abdomen, imaging navigation systems are in the preliminary implementation stage. In this study a multi-stage approach has been developed to support percutaneous liver tumors ablation. It includes CT image acquisition protocol with the amplitude of respiratory motion that yields images subjected to a semi-automatic method able to deliver personalized abdominal model. Then, US probe and ablation needle calibration, as well as patient position adjustment method during the procedure for the preoperative anatomy model, have been combined. Finally, an advanced module for fusion of the preoperative CT with intraoperative US images was designed. These modules have been tested on a phantom and in the clinical environment. The final average Spatial calibration error was 1,7 mm, the average error of matching the position of the markers was about 2 mm during the entire breathing cycle, and average markers fusion error 495 mm. The obtained results indicate the possibility of using the developed method of navigation in clinical practice.


Subject(s)
Abdominal Neoplasms/diagnostic imaging , Ablation Techniques , Liver Neoplasms/diagnostic imaging , Minimally Invasive Surgical Procedures , Radiographic Image Interpretation, Computer-Assisted , Surgery, Computer-Assisted , Tomography, X-Ray Computed , Abdominal Neoplasms/surgery , Anatomic Landmarks , Biopsy, Needle , Humans , Liver Neoplasms/surgery , Patient Care Planning , Patient-Specific Modeling , Phantoms, Imaging , Radiography, Abdominal
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