Image Thresholding Research Articles

Research on steel surface defect detection system based on YOLOv5s-SE-CA model and BEMD image enhancement

ABSTRACT The nondestructive testing capability of steel surface defects has a significant impact on quality of industrial production. However, the single-stage detection network YOLOv5s model for steel surface defect detection is of insufficient feature extraction ability and susceptibility to noise interference in extracting surface defect images. Accordingly, a steel surface defect detection system based on YOLOv5s-SE-CA model and bi-dimensional empirical mode decomposition (BEMD) image enhancement is proposed. Firstly, the BEMD is used for image threshold denoising to improve the quality of surface defect images and achieve preprocessing of the dataset. Secondly, the squeeze and excitation network (SENet) attention mechanism is introduced into the backbone network of YOLOv5s model to adaptively adjust weights of each feature channel. Meanwhile, the coordinate attention (CA) attention mechanism is integrated to improve network model’s ability to focus on surface defects. Therefore, the YOLOv5s-SE-CA model is designed to enhance detection capability of steel surface defects. Experimental verification was carried out using the NEU-DET noisy dataset with specific evaluation indicators of image enhancement detection system. Results showed the proposed system has superior performance when compared to traditional models trained on noisy and denoising datasets and achieved more accurate detection of surface defects in steel.

A complex-valued encoding golden jackal optimization for multilevel thresholding image segmentation

Multilevel thresholding image segmentation is not only an indispensable component of image computation and machine vision but also a fundamental element of image analysis and feature extraction, which has received extensive attention from many domestic and international academics in recent years. However, the threshold level rises in direct proportion to the computational complexity. Therefore, this paper presents a complex-valued encoding golden jackal optimization (CGJO) established on Kapur’s entropy to accomplish this issue, and the intention is to split the available image into a multitude of conspicuous salient portions that illustrate concepts and procedures of the pertinent object. The golden jackal optimization (GJO) employs the cooperated foraging of the jackals to imitate prey searching, enclosing and pouncing to generate the optimum solution. The complex-valued encoding employs the diploid’s notion to alter the real and imaginary of the search agent, which amplifies the information multiplicity and reinforces the inherent parallelism to motivate productive search and inhibit voracious convergence. The functionality and viability of the CGJO are confirmed by comparison with BWOA, DOA, COA, LCA, OOA, SABO, GJO and CRWOA. The experimental results reveal that the CGJO explores and exploits to acquire a superior convergence speed, greater numerical precision and stronger segmentation quality. Additionally, the CGJO offers excellent practicability and stability to address image segmentation successfully.

Pelvic ring fracture in the older adults after minor pelvic trauma – is it an innocent injury?

ObjectiveThis study aimed to evaluate the prevalence of severe hemorrhage as a potentially life-threatening complication in elderly patients with isolated, low-energy pelvic ring fractures, which may be more common than previously described. Patients and methodsA retrospective cohort study was conducted at an academic level 1 trauma center, including 579 patients aged over 65 who suffered from isolated low-energy pelvic fractures between 2006 and 2020. Pelvic computer tomography (CT) scans, with or without contrast, were performed and reviewed for patients with suspected posterior ring injury or bleeding. Patients without CT scans were excluded from the study. ResultsAmong the 123 patients with isolated pubic rami fractures, 30 (24.4 %) had bleeding with a significant hemoglobin drop (>2 g%). 21(70 %) patients of these had posterior pelvic ring involvement as compared to 45 who did not bleed (51 %, p = 0.07). There was a significantly larger proportion of patient who received anticoagulant therapy (ACT) with posterior ring injury among the bleeding group (20 % vs 3.2 % p < 0.01). Treatment included blood transfusion (19/123, 15.5 %), and arterial angiographic embolization (5/123, 4 %). No complications related to angio-embolization were observed, and all patients survived the initial 90-day period. No other source of bleeding was identified in any of these patients. ConclusionSevere pelvic hemorrhage in the older adults due to a minor pelvic injury after a low-energy trauma is not an uncommon complication, especially with combination of ACT and posterior pelvic ring fracture. This indicates that these injuries more challenging than previously believed. Geriatric pelvic ring injuries should be monitored carefully with serial blood counts, and low threshold for imaging including contrast enhanced CT scans and angiography.

Enhanced Chaos Game Optimization for Multilevel Image Thresholding through Fitness Distance Balance Mechanism

Enhanced Chaos Game Optimization for Multilevel Image Thresholding through Fitness Distance Balance Mechanism

BPG-Based Lossy Compression of Three-Channel Remote Sensing Images with Visual Quality Control

A tendency to increase the number of acquired remote sensing images and to make their average size larger has been observed. To manage such data, compression is needed, and lossy compression is often preferable. Since lossy compression introduces distortions, this results in worse classification and object detection. Therefore, lossy compression must be controlled, i.e., the introduced distortions must be under a certain limit. The distortions and the limit can be characterized by different metrics (quantitative criteria). Here, we consider the case of using the HaarPSI metric, which has a very high correlation with visual quality and human attention (saliency map), for three-channel optical band images compressed by the better portable graphics (BPG) encoder, one of the best modern compression techniques. We analyze a two-step procedure of providing a desired visual quality and show its peculiarities for the modes 4:4:4, 4:2:2, and 4:2:0 of image compression. We show how the HaarPSI metric relates to other known metrics of image visual quality and thresholds of distortion visibility. It is demonstrated that the two-step procedure provides about three times better accuracy in providing the desired visual quality compared to the fixed setting of parameter Q that controls compression for the BPG encoder. The provided accuracy is close to the reachable limit determined by the integer value setting of the Q parameter. We also briefly analyze the influence of compression on the classification accuracy of real-life remote sensing data.

Utilizing color analysis and image processing for the assessment of the color of block para rubber

This research explores the use of image processing and color analysis as a reliable approach for evaluating para rubber color quality. An automated color comparator prototype, based on the CIELAB color method and employing various image processing algorithms, is introduced. The research aims to enhance the precision of color analysis for block para rubbers (BPRs), a critical raw material in the rubber industry, impacted by environmental conditions during the sun-drying process. The study follows international standards for BPR specimen preparation and employs an image acquisition system, threshold techniques, and algorithms for color analysis. The proposed method aligns with Rubber Research Institute standards. The outcomes demonstrate the effectiveness of the automated system in accurately assessing the color values of BPRs, providing valuable insights for the rubber industry. This image processing technique offers advantages in natural rubber production for products requiring specific color characteristics.

Image Segmentation Model for Vicinagearth Security Technology of Unmanned Aerial Vehicle Using Improved Pigeon-Inspired Optimization

The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle (UAV) security will become one of the evolving forms of its security technology, and how to improve the segmentation and recognition ability of UAV visual reconnaissance system for maritime targets through improvement will become the key to low-altitude security. Due to the fact that maritime target images are characterized by complex weather, strong interference, high speed requirement and large data volume, the traditional segmentation methods are not suitable for maritime small-target (MST) segmentation and recognition. Therefore, this paper proposes a threshold image segmentation (TIS) method based on an improved pigeon-inspired optimization (PIO) algorithm to provide a better method for segmentation and recognition of MST. First, this study proposes CCPIO based on the horizontal crossover search (HCS) and vertical crossover search (VCS) strategy, which effectively improves the search efficiency of PIO and the ability to jump out of local optimum. And the optimization performance of CCPIO is effectively verified by comparing it with 10 peer algorithms through benchmark function experiments. Further, in this paper, the proposed CCPIO-TIS segmentation model is proposed by combining CCPIO with non-local means, 2D histogram, and Kapur’s entropy. The proposed CCPIO-TIS model is also used for the segmentation and recognition of real MST images, and the results of the experimental comparison and evaluation analysis show that the proposed model has higher quality segmentation results than 12 models of the same type. In summary, this study can provide an efficient and accurate artificial intelligence model for segmentation and recognition of maritime small-target.

Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography

Objective. With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset. Approach. The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml−1) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data. Main Results. Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77 ± 30.64) compared to threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms. Signficance. In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.

Copy-move forgery detection using Regional Density Center clustering

Copy-move forgery detection is a common image tampering detection technology. In this paper, a novel copy-move forgery detection scheme is proposed. The proposed scheme is based on Regional Density Center (RDC) clustering and Refined Length Homogeneity Filtering (RLHF) policy. First, to obtain an adequate number of keypoints in smooth or small areas of the image, the proposed scheme employs scale normalization and adjustment of the contrast threshold of the input image. Subsequently, to speed up the feature matching process, a matching algorithm based on gray value grouping is used to match the keypoints. RLHF policy is applied to filter the mismatched pairs. To guarantee a good estimation of the affine transformation, the RDC clustering algorithm is proposed to group the matched pairs. Finally, the correlation coefficients are computed to precisely locate the tampered regions. The proposed copy-move forgery detection scheme based on RDC and RLHF can effectively identify duplicated regions of digital images. It demonstrates the effectiveness and robustness of the proposed scheme over many state-of-the-art schemes on public datasets.

Pengolahan Citra Digital EKG Rumah Sakit Tk.IV Samarinda

In the hospital itself, especially in the MCU Poly Radiology Installation, it is a unit that performs services such as Medical Check Up, making health certificates, and carrying out routine checks at an institution. ECG examination is one of the services most frequently performed routinely by patients. Examinations carried out include medical check-ups, monitoring of long-term therapy, patient to assessment before surgery, individual screening for high-risk jobs, examinations before participating in sporting events, and overall examinations as a condition of employment. This study carried out the process of withdrawing information or object descriptions or identifying objects contained in images by compressing or reducing image data. By using EKG data from TK.IV Samarinda Hospital by carrying out processing stages such as converting analog data to digital, cropping, and applying filters to EKG images. After the filter is applied to the image, it is found that after 5 types of image processing (RGB Image, Grayscale Image, Binary Image, Thresholding Image, and Edge Detection Image) on the results of the ECG signal it can be concluded that the processed signal retains the features of the existing signal and information. . Of the five types of processing methods, the clearest result is conversion to Edge Detection imagery.

Modified snake optimizer based multi-level thresholding for color image segmentation of agricultural diseases

Image segmentation is crucial for early identification and diagnosis of crop diseases in agriculture. It helps identify disease areas and characteristics, laying the groundwork for disease diagnosis and treatment. However, color image thresholding segmentation method faces challenges in finding the optimal threshold as the number of thresholds increases, which affects segmentation quality. In this paper, a modified snake optimizer (MSO) is proposed to address the color image thresholding segmentation problem using Kapur’s entropy as the objective function. MSO incorporates a dynamic adaptive parameter adjustment method. The improved global position update formula introduces guidance from the optimal individual and disturbance from random individuals, effectively achieving a balance between global and local search capabilities. A dynamic parameter adjustment method is added to accelerate convergence speed during snake movement to food. Lévy flight is introduced to the Flight mode to help the algorithm escape local extremums. A balancing strategy is implemented in the Mating mode, incorporating guidance from the optimal individual and information exchange between sub-populations, enabling balanced exploration and local exploitation capabilities. The hill-climbing jump operation for the optimal individual is added to help the algorithm overcome local stagnation. The proposed MSO is evaluated on CEC 2017 test functions and color test images, and the obtained results are compared with other segmentation methods and other intelligent optimization algorithms. The results demonstrate that MSO exhibits stable and excellent performance in both complex global optimization problems and color image thresholding segmentation problems. Finally, MSO is applied to segment rice disease images, and the results reveal significantly better segmentation performance compared to other algorithms. MSO shows great potential in solving the thresholding segmentation problem for agricultural disease images, thereby assisting in the accurate identification, diagnosis, and treatment of agricultural diseases and insect pests.

Enhancing image thresholding segmentation with a novel hybrid battle royale optimization algorithm

Enhancing image thresholding segmentation with a novel hybrid battle royale optimization algorithm

Reversible Data Hiding in Encrypted Images Based on Preprocessing-Free Variable Threshold Secret Sharing

To solve the limitations of reversible data hiding in encrypted domains (RDH-ED) that cannot be applied to a distributed variable security environment, a novel RDH-ED scheme based on variable threshold image secret sharing (VTSIS) is proposed. Initially, a security vulnerability analysis of existing changing thresholds in a bivariate polynomial-based secret image sharing (TCSIS) method is conducted and validated through experiments. Subsequently, enhancements are made to the VTSIS scheme to rectify the identified security loopholes. During the image encryption process, additional data can be embedded into the redundancy of VTSIS, which results in a large embedding rate and high security. Finally, theoretical analysis and experimental proofs are carried out for the proposed scheme, and the results show that our scheme broadens the application scenarios of RDH-ED. Notably, the scheme eliminates the need for preprocessing and has the advantages of high security, a large embedding rate, and complete reversibility.

DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation

Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm's ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation.

Semi-automated software improves interrater reliability and reduces processing time of magnetic resonance imaging-based exocrine pancreatic assessments in pediatric patients

ObjectivesMagnetic resonance (MR) imaging with secretin stimulation (MR-PFTs) is a non-invasive test for pancreatic exocrine function based on assessing the volume of secreted bowel fluid in vivo. Adoption of this methodology in clinical care and research is largely limited to qualitative assessment of secretion as current methods for secretory response quantification require manual thresholding and segmentation of MR images, which can be time-consuming and prone to interrater variability. We describe novel software (PFTquant) that preprocesses and thresholds MR images, performs heuristic detection of non-bowel fluid objects, and provides the user with intuitive semi-automated tools to segment and quantify bowel fluid in a fast and robust manner. We evaluate the performance of this software on a retrospective set of clinical MRIs.MethodsTwenty MRIs performed in children (< 18 years) were processed independently by two observers using a manual technique and using PFTquant. Interrater agreement in measured secreted fluid volume was compared using intraclass correlation coefficients, Bland-Altman difference analysis, and Dice similarity coefficients.ResultsInterrater reliability of measured bowel fluid secretion using PFTquant was 0.90 (0.76–0.96 95% C.I.) with − 4.5 mL mean difference (-39.4–30.4 mL 95% limits of agreement) compared to 0.69 (0.36–0.86 95% C.I.) with − 0.9 mL mean difference (-77.3–75.5 mL 95% limits of agreement) for manual processing. Dice similarity coefficients were better using PFTquant (0.88 +/- 0.06) compared to manual processing (0.85 +/- 0.10) but not significantly (p = 0.11). Time to process was significantly (p < 0.001) faster using PFTquant (412 +/- 177 s) compared to manual processing (645 +/- 305 s).ConclusionNovel software provides fast, reliable quantification of secreted fluid volume in children undergoing MR-PFTs. Use of the novel software could facilitate wider adoption of quantitative MR-PFTs in clinical care and research.

Slime mould algorithm with mechanism of leadership and self-phagocytosis for multilevel thresholding of color image

Threshold based segmentation method is widely used because of its simplicity, high efficiency and easy implementation. However, as the number of thresholds increases, the algorithm's search space also increases, increasing computational complexity, which reduces computational efficiency and segmentation accuracy. Additionally, the existing multi-threshold (MT) segmentation methods still struggle with slow convergence speed and low solution accuracy. The optimum threshold for the image is determined in this study using the Slime Mould Algorithm with the Mechanism of Leadership and Self-phagocytosis (SMA-MLS). Firstly, the initial population production method based on Logistic-Tent mapping is introduced to enhance the diversity of algorithm solutions. Secondly, the position update formula with leadership mechanism is proposed to improve the convergence speed and accuracy. In addition, the adaptive combined mutation mechanism balances the exploration and exploitation ability. Finally, the self-phagocytosis mechanism maintains population diversity and heritability. A series of benchmark test suites are employed to evaluate the performance of the algorithm. Experimental results demonstrate that SMA-MLS does show good performance. It is superior in terms of convergence accuracy, convergence speed and stability. In the MT segmentation experiments, by using Kapur as the objective function and working with color images, SMA-MLS proved to be effective in the MT segmentation problem of images. Moreover, SMA-MLS will be applied to mechanical parameter optimization, neural network and other fields in future research.

An Image-Based Prior Knowledge-Free Approach for a Multi-Material Decomposition in Photon-Counting Computed Tomography.

Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water.

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.

Evaluation and Management of Thoracolumbar Spine Trauma in Pediatric Patients: A Critical Analysis Review.

» Pediatric thoracolumbar trauma, though rare, is an important cause of morbidity and mortality and necessitates early, accurate diagnosis and management.» Obtaining a detailed history and physical examination in the pediatric population can be difficult. Therefore, the threshold for advanced imaging, such as magnetic resonance imaging, is low and should be performed in patients with head injuries, altered mental status, inability to cooperate with examination, and fractures involving more than 1 column of the spine.» The classification of pediatric thoracolumbar trauma is based primarily on adult studies and there is little high-level evidence examining validity and accuracy in pediatric populations.» Injury pattern and neurologic status of the patient are the most important factors when determining whether to proceed with operative management.

Circulating tumour DNA fraction as a predictor of treatment efficacy in a randomized phase 2 trial of [177Lu]Lu-PSMA-617 (LuPSMA) versus cabazitaxel in metastatic castration-resistant prostate cancer (mCRPC) progressing after docetaxel (TheraP ANZUP 1603).

5055 Background: The TheraP trial showed that for patients with PSMA-positive, non-FDG-discordant mCRPC progressing after docetaxel, LuPSMA significantly improved biochemical and objective response rates, progression-free survival (PFS), and quality of life compared to cabazitaxel. While subsequent studies nominated high PSMA SUVmean to inform patient prioritization for LuPSMA, limited genomic data exists to guide optimal selection of these two life-prolonging therapies. We present an exploratory analysis of ctDNA fraction (ctDNA%) in baseline samples from the TheraP trial. Methods: We analyzed 180 baseline blood samples from participants who received ≥1 cycle of protocol-assigned treatment. Plasma cell-free DNA and matched white blood cell DNA underwent targeted sequencing to estimate ctDNA%. Prespecified ctDNA% categories (&lt;2%, 2-30%, and &gt;30%) were associated with previously validated imaging thresholds (PSMA SUVmean ≥10 and FDG metabolic tumor volume [MTV] ≥200mL), rate of PSA reduction ≥50% (PSA50-RR), and PFS, using chi-square test, logistic regression and Cox regression, respectively. Results: ctDNA% was evaluable in 178 (99%) participants, with ctDNA ≥2% in 85%. Median ctDNA% was 28% in ctDNA≥2% samples, and balanced across treatment arms. The odds of a PSA50 response to LuPSMA vs cabazitaxel were significantly higher for men with ctDNA &lt;2% (OR infinite, p=0.0067; PSA50-RR 100% vs 58%), with no difference at ctDNA &gt;30% (OR 1.1, p=1.0; PSA50-RR 46% vs 44%). Higher ctDNA% was associated with shorter PFS in LuPSMA- but not cabazitaxel-treated patients (LuPSMA: HR 5.0, p&lt;0.001 for ctDNA &lt;2% vs &gt;30%; cabazitaxel: HR 1.4, p=0.35 for ctDNA &lt;2% vs &gt;30%; interaction p=0.035). The predictive potential of ctDNA% was additive to PSMA SUVmean in LuPSMA-treated patients. Higher ctDNA% categories were enriched for patients with high FDG MTV and low PSMA SUVmean disease (Table). Conclusions: ctDNA% is a candidate predictive and prognostic biomarker for differential response to LuPSMA versus taxane chemotherapy in patients with molecular imaging-selected mCRPC progressing after docetaxel. Clinical trial information: NCT03392428 . [Table: see text]