A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation.
Ontology highlight
ABSTRACT: Purpose
We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies.Procedures
Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2-3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology.Results
While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (rnecrotic = 0.92, rperi-necrotic = 0.82 and rviable = 0.98) with the histology.Conclusions
The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures.
SUBMITTER: Katiyar P
PROVIDER: S-EPMC5332060 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ImportanceThe utilization of artificial intelligence for the differentiation of benign and malignant breast lesions in multiparametric MRI (mpMRI) assists radiologists to improve diagnostic performance.ObjectivesTo develop an automated deep learning model for breast lesion segmentation and characterization and to evaluate the characterization performance of AI models and radiologists.Materials and methodsFor lesion segmentation, 2,823 patients were used for the training, validation, and testing of the VNet-based segmentation models, and the average Dice similarity coefficient (DSC) between the manual segmentation by radiologists and the mask generated by VNet was calculated. For lesion characterization, 3,303 female patients with 3,607 pathologically confirmed lesions (2,213 malignant and 1,394 benign lesions) were used for the three ResNet-based characterization models (two single-input and one multi-input models). Histopathology was used as the diagnostic criterion standard to assess the characterization performance of the AI models and the BI-RADS categorized by the radiologists, in terms of sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). An additional 123 patients with 136 lesions (81 malignant and 55 benign lesions) from another institution were available for external testing.ResultsOf the 5,811 patients included in the study, the mean age was 46.14 (range 11-89) years. In the segmentation task, a DSC of 0.860 was obtained between the VNet-generated mask and manual segmentation by radiologists. In the characterization task, the AUCs of the multi-input and the other two single-input models were 0.927, 0.821, and 0.795, respectively. Compared to the single-input DWI or DCE model, the multi-input DCE and DWI model obtained a significant increase in sensitivity, specificity, and accuracy (0.831 vs. 0.772/0.776, 0.874 vs. 0.630/0.709, 0.846 vs. 0.721/0.752). Furthermore, the specificity of the multi-input model was higher than that of the radiologists, whether using BI-RADS category 3 or 4 as a cutoff point (0.874 vs. 0.404/0.841), and the accuracy was intermediate between the two assessment methods (0.846 vs. 0.773/0.882). For the external testing, the performance of the three models remained robust with AUCs of 0.812, 0.831, and 0.885, respectively.ConclusionsCombining DCE with DWI was superior to applying a single sequence for breast lesion characterization. The deep learning computer-aided diagnosis (CADx) model we developed significantly improved specificity and achieved comparable accuracy to the radiologists with promise for clinical application to provide preliminary diagnoses.
| S-EPMC9402900 | biostudies-literature
Project description:IntroductionExternal radiotherapy is a major treatment for localized prostate cancer (PCa). Dose escalation to the whole prostate gland increases biochemical relapse-free survival but also acute and late toxicities. Dose escalation to the dominant index lesion (DIL) only is of growing interest. It requires a robust delineation of the DIL. In this context, we aimed to evaluate the inter-observer variability of DIL delineation.Material and methodsTwo junior radiologists and a senior radiation oncologist delineated DILs on 64 mpMRIs of patients with histologically confirmed PCa. For each mpMRI and each reader, eight individual DIL segmentations were delineated. These delineations were blindly performed from one another and resulted from the individual analysis of the T2, apparent diffusion coefficient (ADC), b2000, and dynamic contrast enhanced (DCE) sequences, as well as the analysis of combined sequences (T2ADC, T2ADCb2000, T2ADCDCE, and T2ADCb2000DCE). Delineation variability was assessed using the DICE coefficient, Jaccard index, Hausdorff distance measure, and mean distance to agreement.ResultsT2, ADC, T2ADC, b2000, T2 + ADC + b2000, T2 + ADC + DCE, and T2 + ADC + b2000 + DCE sequences obtained DICE coefficients of 0.51, 0.50, 0.54, 0.52, 0.54, 0.55, 0.53, respectively, which are significantly higher than the perfusion sequence alone (0.35, p < 0.001). The analysis of other similarity metrics lead to similar results. The tumor volume and PI-RADS classification were positively correlated with the DICE scores.ConclusionOur study showed that the contours of prostatic lesions were more reproducible on certain sequences but confirmed the great variability of prostatic contours with a maximum DICE coefficient calculated at 0.55 (joint analysis of T2, ADC, and perfusion sequences).
| S-EPMC10741937 | biostudies-literature
Project description:To evaluate the feasibility of using strain-encoded (SENC) breast magnetic resonance images (MRI) for breast cancer detection by examining the compression and relaxation response properties in phantoms and ex vivo breast samples.A tissue phantom was constructed to mimic different sizes of breast masses and tissue stiffness. In addition, five human ex vivo whole breast specimens with and without masses were studied. MR data was acquired on a 3T scanner consisting of T(1)-weighted, fat suppressed spin echo T(2)-weighted, and SENC breast images. Mechanical tissue characteristics (strain) of the phantoms and breast tissue samples were measured using SENC imaging in both compression and relaxation modes. The breast tissue specimens were sectioned and stained in the same plane as the MRI for histological evaluation.For the phantom, SENC images showed soft masses with quantitative strain values between 35% and 50%, while harder masses had strain values between 0% and 20%. Combined compression (CMP) and relaxation (REX) breast SENC images separately categorized all masses into three different groups. For breast SENC, the signal intensities between ex vivo breast mass and breast glandular tissue were significantly different (-7.6 ± 2.6 verses -20.6 ± 5.4 for SENC-CMP, and 4.2 ± 1.5 verses 22.6 ± 5 for SENC-REX, p < 0.05).We have demonstrated that SENC breast MRI can be used to obtain mechanical tissue properties and give quantitative estimates of strain in tumors. This feasibility study provides the basis for future clinical studies.
| S-EPMC3528768 | biostudies-other
Project description:Prostate segmentation in multiparametric magnetic resonance imaging (mpMRI) can help to support prostate cancer diagnosis and therapy treatment. However, manual segmentation of the prostate is subjective and time-consuming. Many deep learning monomodal networks have been developed for automatic whole prostate segmentation from T2-weighted MR images. We aimed to investigate the added value of multimodal networks in segmenting the prostate into the peripheral zone (PZ) and central gland (CG). We optimized and evaluated monomodal DenseVNet, multimodal ScaleNet, and monomodal and multimodal HighRes3DNet, which yielded dice score coefficients (DSC) of 0.875, 0.848, 0.858, and 0.890 in WG, respectively. Multimodal HighRes3DNet and ScaleNet yielded higher DSC with statistical differences in PZ and CG only compared to monomodal DenseVNet, indicating that multimodal networks added value by generating better segmentation between PZ and CG regions but did not improve the WG segmentation. No significant difference was observed in the apex and base of WG segmentation between monomodal and multimodal networks, indicating that the segmentations at the apex and base were more affected by the general network architecture. The number of training data was also varied for DenseVNet and HighRes3DNet, from 20 to 120 in steps of 20. DenseVNet was able to yield DSC of higher than 0.65 even for special cases, such as TURP or abnormal prostate, whereas HighRes3DNet's performance fluctuated with no trend despite being the best network overall. Multimodal networks did not add value in segmenting special cases but generally reduced variations in segmentation compared to the same matched monomodal network.
| S-EPMC7596462 | biostudies-literature
Project description:Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain, our approach learns to segment multi-modal (T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a variational auto-encoder (VAE) to disentangle the image content from style. The VAE constrains the style feature encoding to match a universal prior (Gaussian) that is assumed to span the styles of all the source and target modalities. The extracted image style is converted into a latent style scaling code, which modulates the generator to produce multi-modality images according to the target domain code from the image content features. Finally, we introduce a joint distribution matching discriminator that combines the translated images with task-relevant segmentation probability maps to further constrain and regularize image-to-image (I2I) translations. We performed extensive comparisons to multiple state-of-the-art I2I translation and segmentation methods. Our approach resulted in the lowest average multi-domain image reconstruction error of 1.34±0.04. Our approach produced an average Dice similarity coefficient (DSC) of 0.85 for T1w and 0.90 for T2w MRI for multi-organ segmentation, which was highly comparable to a fully supervised MRI multi-organ segmentation network (DSC of 0.86 for T1w and 0.90 for T2w MRI).
| S-EPMC7757792 | biostudies-literature
Project description:Validation data for segmentation algorithms dedicated to preclinical images is fiercely lacking, especially when compared to the large number of databases of Human brain images and segmentations available to the academic community. Not only is such data essential for validating methods, it is also needed for objectively comparing concurrent algorithms and detect promising paths, as segmentation challenges have shown for clinical images. The dataset we present here is a first step in this direction. It comprises 10 T2-weighted MRIs of healthy adult macaque brains, acquired on a 7 T magnet, along with corresponding manual segmentations into 17 brain anatomic labelled regions spread over 5 hierarchical levels based on a previously published macaque atlas (Calabrese et al., 2015) [1]. By giving access to this unique dataset, we hope to provide a reference needed by the non-human primate imaging community. This dataset was used in an article presenting a new primate brain morphology analysis pipeline, Primatologist (Balbastre et al., 2017) [2]. Data is available through a NITRC repository (https://www.nitrc.org/projects/mircen_macset).
| S-EPMC5686468 | biostudies-literature
Project description:OBJECTIVES:Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. METHODS:We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. RESULTS:The DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE. CONCLUSIONS:The DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity. KEY POINTS:• Deep learning allows for accurate meningioma detection and segmentation • Deep learning helps clinicians to assess patients with meningiomas • Meningioma monitoring and treatment planning can be improved.
| S-EPMC6291436 | biostudies-literature
Project description:ObjectivesRenal multiparametric MRI (mpMRI) is a promising tool to monitor renal allograft health to enable timely treatment of chronic allograft nephropathy. This study aims to validate mpMRI by whole-kidney histology following transplantectomy.Materials and methodsA patient with kidney transplant failure underwent mpMRI prior to transplantectomy. The mpMRI included blood oxygenation level-dependent (BOLD) MRI, T1 and T2 mapping, diffusion-weighted imaging (DWI), 2D phase contrast (2DPC) and arterial spin labeling (ASL). Parenchymal mpMRI measures were compared to normative values obtained in 19 healthy controls. Differences were expressed in standard deviations (SD) of normative values. The mpMRI measures were compared qualitatively to histology.ResultsThe mpMRI showed a heterogeneous parenchyma consistent with extensive interstitial hemorrhage on histology. A global increase in T1 (+ 3.0 SD) and restricted diffusivity (- 3.6 SD) were consistent with inflammation and fibrosis. Decreased T2 (- 1.8 SD) indicated fibrosis or hemorrhage. ASL showed diminished cortical perfusion (- 2.9 SD) with patent proximal arteries. 2DPC revealed a 69% decrease in renal perfusion. Histological evaluation showed a dense inflammatory infiltrate and fibrotic changes, consistent with mpMRI results. Most interlobular arteries were obliterated while proximal arteries were patent, consistent with ASL findings.DiscussionmpMRI findings correlated well with histology both globally as well as locally.
| S-EPMC8154819 | biostudies-literature
Project description:Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ?10° for the primary fiber direction and ?20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy.
| S-EPMC5732036 | biostudies-literature
Project description:BackgroundMultiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose (18F-FDG)-PET.MethodsThe CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used.ResultsIn 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. 18F-FDG-PET and morphologic features were less predictive.ConclusionOur CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions.
| S-EPMC6486931 | biostudies-literature