Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.
In the present study of more than 1200 patients and 7 years of experience, RSL was shown to facilitate breast- and axilla-conserving surgery in a diverse patient population. There was a significant reduction in resection volume while maintaining low positive resection margin rates after BCS.
BREAST IMAGINGB reast cancer screening using mammography has been implemented in many countries, which has resulted in reduced breast cancer mortality rates (1,2). Despite all efforts, a stable rate of approximately 30% of breast cancers still manifest between screening rounds. Such interval cancers (ICs) often have a worse prognosis than do screeningdetected cancers (3).Nevertheless, prior studies have shown that approximately half of ICs could be retrospectively identified by visual inspection of the last screening images as obvious false-negative findings or with minimal signs (4). The sensitivity of mammography in breast cancer detection decreases with increasing density of the breast tissue due to masking by the increasing amount of fibroglandular tissue within the breast (5). Furthermore, women with dense breasts have a higher risk of developing breast cancer (6). Consequently, the incidence of IC also increases with higher breast density (BD) (7). Studies have shown that both automated and clinical Breast Imaging Reporting and Data System density similarly enable prediction of interval and screen-detected cancer risk (8), which is now integrated in several existing breast cancer prediction models among other classic risk factors, such as age, ethnicity, family history of breast cancer, and history of breast biopsy (9-11).Artificial intelligence (AI) models for breast cancer detection based on deep learning technology are proposed as a clinical tool that could potentially increase both quality and efficiency of breast cancer screening. Several reader studies have demonstrated improved performance of the radiologist when AI was used for support during evaluation of both mammography and tomosynthesis images (12-17). The suggested applications of AI include replacing one reader in a double reading screening program (18,19) or for performing study triage to distinguish low-from high-risk findings, aiming to exclude low-risk studies from double human reading (20-22). Although the clinical evidence Background: Inclusion of mammographic breast density (BD) in breast cancer risk models improves accuracy, but accuracy remains modest. Interval cancer (IC) risk prediction may be improved by combining assessments of BD and an artificial intelligence (AI) cancer detection system.Purpose: To evaluate the performance of a neural network (NN)-based model that combines the assessments of BD and an AI system in the prediction of risk of developing IC among women with negative screening mammography results. Materials and Methods:This retrospective nested case-control study performed with screening examinations included women who developed IC and women with normal follow-up findings (from January 2011 to January 2015). An AI cancer detection system analyzed all studies yielding a score of 1-10, representing increasing likelihood of malignancy. BD was automatically computed using publicly available software. An NN model was trained by combining the AI score and BD using 10-fold cross-validation. Bootstrap analysis was used to c...
The presented surgical navigation system improved the intra-operative awareness about tumor position and orientation, with the potential to improve surgical outcomes for non-palpable breast tumors. Results are positive, and participating surgeons were enthusiastic, but extended surgical experience on real breast tissue is required.
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