Breast cancer, imaging processing, image registration

This project addresses the global challenge of breast cancer, particularly in low-resource settings, by creating a pioneering mammography database. Breast cancer, identified by the World Health Organization as a leading cause of cancer death among women, often faces diagnostic and treatment resource constraints in low- and middle-income countries. To enhance early diagnosis and address educational setbacks, the project focuses on leveraging artificial intelligence (AI) technologies through a comprehensive database...

This study was conducted to identify the risk causes and predictive models based on the clinical features of patients with breast cancer classified as triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBCs) using Korean cancer statistics. A total of 2045 cases that underwent three types of hormone receptor tests were obtained from Korean cancer data in 2016. Research data were analyzed with the software SPSS Ver. 26.0. TNBC and non-TNBCs accounted for 12.4% and 87.6% of the data, respectively...

BACKGROUND: Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model. METHODS: To aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration...

BACKGROUND: Breast cancer is the leading cause of cancer-related mortality among women worldwide. The incidence and mortality increased globally since starting registration in 1990. Artificial intelligence is being widely experimented in aiding in breast cancer detection, radiologically or cytologically. It has a beneficial role in classification when used alone or combined with radiologist evaluation. The objectives of this study are to evaluate the performance and accuracy of different machine learning algorithms in diagnostic mammograms using a local four-field digital mammogram dataset...

OBJECTIVE: A generative adversarial network (TCBCTNet) was proposed to generate synthetic computed tomography (sCT) from truncated low-dose cone-beam computed tomography (CBCT) and planning CT (pCT). The sCT was applied to the dose calculation of radiotherapy for patients with breast cancer. METHODS: The low-dose CBCT and pCT images of 80 female thoracic patients were used for training. The CBCT, pCT, and replanning CT (rCT) images of 20 thoracic patients and 20 patients with breast cancer were used for testing...

BACKGROUND: Ki-67 is a key indicator of the proliferation activity of tumors. However, no standardized criterion has been established for Ki-67 index calculation. Scale-invariant feature transform (SIFT) algorithm can identify the robust invariant features to rotation, translation, scaling and linear intensity changes for matching and registration in computer vision. Thus, this study aimed to develop a SIFT-based computer-aided system for Ki-67 calculation in breast cancer. METHODS: Hematoxylin and eosin (HE)-stained and Ki-67-stained slides were scanned and whole slide images (WSIs) were obtained...

Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing...

Breast cancer is one of the most common malignancies. Pathological image processing of breast has become an important means for early diagnosis of breast cancer. Using medical image processing to assist doctors to detect potential breast cancer as early as possible has always been a hot topic in the field of medical image diagnosis. In this paper, a breast cancer recognition method based on image processing is systematically expounded from four aspects: breast cancer detection, image segmentation, image registration, and image fusion...

BACKGROUND: The strategy to combat the problem associated with large deformations in the breast due to the difference in the medical imaging of patient posture plays a vital role in multimodal medical image registration with artificial intelligence (AI) initiatives. How to build a breast biomechanical model simulating the large-scale deformation of soft tissue remains a challenge but is highly desirable. METHODS: This study proposed a hybrid individual-specific registration model of the breast combining finite element analysis, property optimization, and affine transformation to register breast images...

BACKGROUND AND OBJECTIVES: Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography (CT) images remains challenging due to the larger displacements compared to those in brain or prostate Magnetic Resonance Imaging datasets that are typically considered as benchmarks. In this study, we investigate the use of the commonly used unsupervised deep learning framework VoxelMorph for the registration of a longitudinal abdominopelvic CT dataset acquired in patients with bone metastases from breast cancer...

Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localization and lower the radiation dose delivered to the surrounding healthy tissues. This study proposes a novel image registration method dedicated to breast tumor bed localization addressing the problem of missing data due to tumor resection that may be applied to real-time radiotherapy planning...

BACKGROUND: Breast cancer is the most common cancer in women and the first cancer concerning mortality. Metastatic breast cancer remains a disease with a poor prognosis and about 30% of women diagnosed with an early stage will have a secondary progression. Metastatic breast cancer is an incurable disease despite significant therapeutic advances in both supportive cares and targeted specific therapies. In the management of a metastatic patient, each clinician follows a highly complex and strictly personal decision making process...

PURPOSE:  The technically caused delay between low-energy (LE) and high-energy (HE) acquisitions allows motion artifacts in contrast-enhanced dual-energy mammography (CEDEM). In this study the effect of motion correction by nonrigid registration on image quality of the recombined images was investigated. MATERIALS AND METHODS:  Retrospectively for 354 recombined CEDEM images an additional recombined image was processed from the raw data of LE and HE images using the motion correction algorithm...

BACKGROUND: We devised a breast-conserving surgery (BCS) utilizing a new image-processing and projection technique using a radiation treatment planning system (RTPS) and deformable image registration (DIR) for patients with breast cancer after neoadjuvant chemotherapy (NAC). RTPSs and DIR are commonly used in planning radiation treatment. The purpose of this pilot study was to evaluate the feasibility of our procedure. PATIENTS AND METHODS: Twenty-six patients diagnosed with breast cancer underwent NAC and BCS between November 2014 and May 2020...

Chemotherapy for non-central nervous system cancers is associated with abnormalities in brain structure and function. Diffusion tensor imaging (DTI) allows for studying in vivo microstructural changes in brain white matter. Tract-based spatial statistics (TBSS) is a widely used processing pipeline in which DTI data are typically normalized to a generic DTI template and then 'skeletonized' to compensate for misregistration effects. However, this approach greatly reduces the overall white matter volume that is subjected to statistical analysis, leading to information loss...

OBJECTIVE: According to the European Reference Organization for Quality Assured Breast Cancer Screening and Diagnostic Services (EUREF) image quality in mammography is assessed by recording and analyzing a set of images of the CDMAM phantom. The EUREF procedure applies an automated analysis combining image registration, signal detection and nonlinear fitting. We present a proof of concept for an end-to-end deep learning framework that assesses image quality on the basis of single images as an alternative...

BACKGROUND: Breast cancer is one of the most important malignant tumors among women causing a serious impact on women's lives and mammography is one the most important methods for breast examination. When diagnosing the breast disease, radiologists sometimes may consult some previous diagnosis cases as a reference. But there are many previous cases and it is important to find which cases are the similar cases, which is a big project costing lots of time. Medical image retrieval can provide objective reference information for doctors to diagnose disease...

PURPOSE: Radiation dose to cardiac substructures is related to radiation-induced heart disease. However, substructures are not considered in radiation therapy planning (RTP) due to poor visualization on CT. Therefore, we developed a novel deep learning (DL) pipeline leveraging MRI's soft tissue contrast coupled with CT for state-of-the-art cardiac substructure segmentation requiring a single, non-contrast CT input. MATERIALS/METHODS: Thirty-two left-sided whole-breast cancer patients underwent cardiac T2 MRI and CT-simulation...

Mass spectrometry imaging (MSI) is an analytical technique for the unlabeled and multiplex imaging of molecules in biological tissue sections. It therefore enables the spatial and molecular annotations of tissues complementary to histology. It has already been shown that MSI can guide subsequent material isolation technologies such as laser microdissection (LMD) to enable a more in-depth molecular characterization of MSI-highlighted tissue regions. However, with MSI now reaching spatial resolutions at the single-cell scale, there is a need for a precise co-registration between MSI and the LMD...

BACKGROUND AND OBJECTIVE: Massive work by distinguished researchers in the domain of breast segmentation has been proposed. However, no significant solution reduces the limitations of the false positive rate of cancerous cells in the breast body for probing the abnormalities of particular features. This problem is challenging in its nature and essential to be solved. It is needed to reach the optimal measurements of the breast parenchyma, the breast patchy regions of the mammogram, or the breast registration for searching of precise oddities...