MRI image analysis methods and applications: an algorithmic perspective using brain tumors as an exemplar

Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI

OBJECTIVES

While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data.

METHODS

In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients). Baseline segmentation models were trained and employed within a semi-supervised learning (SSL) framework to refine the pseudo-masks. Following each self-refinement cycle, a new model was trained and tested on a held-out set of 319 manually segmented image slices (93 adult patients), with the SSL cycles continuing until Dice score coefficient (DSC) peaked. DSCs were compared using bootstrap resampling. Utilizing the best-performing models, two inference methods were compared: (1) conventional full-image segmentation, and (2) a hybrid method augmenting full-image segmentation with detection plus image patch segmentation.

RESULTS

Baseline segmentation models achieved DSC of 0.768 (U-Net), 0.831 (Mask R-CNN), and 0.838 (HRNet), improving with self-refinement to 0.798, 0.871, and 0.873 (each p < 0.001), respectively. Hybrid inference outperformed full image segmentation alone: DSC 0.884 (Mask R-CNN) vs. 0.873 (HRNet), p < 0.001.

CONCLUSIONS

Line annotations mined from PACS can be harnessed within an automated pipeline to produce accurate brain MRI tumor segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

KEY POINTS

• A brain MRI tumor detection model trained using clinical line measurement annotations mined from PACS was leveraged to automatically generate tumor segmentation pseudo-masks. • An iterative self-refinement process automatically improved pseudo-mask quality, with the best-performing segmentation pipeline achieving a Dice score of 0.884 on a held-out test set. • Tumor line measurement annotations generated in routine clinical radiology practice can be harnessed to develop high-performing segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

Atomic-Resolution STEM Image Denoising by Total Variation Regularization

Atomic-resolution electron microscopy imaging of solid state material is a powerful method for structural analysis. Scanning transmission electron microscopy (STEM) is one of the actively used techniques to directly observe atoms in materials. However, some materials are easily damaged by the electron beam irradiation, and only noisy images are available when we decrease the electron dose to avoid beam damages. Therefore, a denoising process is necessary for precise structural analysis in low-dose STEM. In this study, we propose total variation (TV) denoising algorithm to remove quantum noise in a STEM image. We defined an entropy of STEM image that corresponds to the image contrast to determine a hyperparameter and we found that there is a hyperparameter that maximize the entropy. We acquired atomic resolution STEM image of CaF2 viewed along the [001] direction, and executed TV denoising. The atomic columns of Ca and F are clearly visualized by the TV denoising, and atomic position of Ca and F are determined with the error of ± 1 pm and ± 4 pm, respectively.

Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features

Malignancy of the brain and CNS is unfortunately a common diagnosis. A large subset of these lesions tends to be high grade tumors which portend poor prognoses and low survival rates, and are estimated to be the tenth leading cause of death worldwide. The complex nature of the brain tissue environment in which these lesions arise offers a rich opportunity for translational research. Magnetic Resonance Imaging (MRI) can provide a comprehensive view of the abnormal regions in the brain, therefore, its applications in the translational brain cancer research is considered essential for the diagnosis and monitoring of disease. Recent years has seen rapid growth in the field of radiogenomics, especially in cancer, and scientists have been able to successfully integrate the quantitative data extracted from medical images (also known as radiomics) with genomics to answer new and clinically relevant questions. In this paper, we took raw MRI scans from the REMBRANDT data collection from public domain, and performed volumetric segmentation to identify subregions of the brain. Radiomic features were then extracted to represent the MRIs in a quantitative yet summarized format. This resulting dataset now enables further biomedical and integrative data analysis, and is being made public via the NeuroImaging Tools & Resources Collaboratory (NITRC) repository ( https://www.nitrc.org/projects/rembrandt_brain/ ).

[Mn2+-doped Prussian blue nanoparticles for T1-T2 dual-mode magnetic resonance imaging and photothermal therapy in vitro]

OBJECTIVE

To prepare Mn²⁺-doped Prussian blue nanoparticles (Mn-PB NPs) for T1-T2 dual-mode magnetic resonance imaging (MRI) and photothermal therapy in vitro.

OBJECTIVE

Mn-PB NPs were prepared based on manganese chloride, ferrous chloride and potassium ferricyanide using the microemulsion method. The performance of T1-T2 dual-mode MRI with Mn-PB NPs and the photothermal property of the nanoparticles were assessed. CCK-8 assay and AM/PI double staining were used to evaluate the effect of photothermal therapy in vitro using the parepared nanoparticles.

OBJECTIVE

The prepared Mn-PB NPs had a mean particle size of 39.46±0.42 nm with a Zeta potential of -25.9±1.2 mV and exhibited a good dispersibility and uniform particle size. In MRI using the nanoparticles, the r1 and r2 values reached 0.68 and 3.65 (mmol/L)^-1s^-1, respectively, indicating good performance of Mn-PB NPs for T1 and T2 enhancement in MRI. When irradiated with 808 nm laser for 10 min, Mn-PB NPs showed a temperature rise to 90 ℃ to cause significant reduction of cell survival. CCK-8 assay and AM/PI double staining confirmed that Mn-PB NPs were capable of efficient killing of HepG2 cells upon 808 nm laser irradiation.

OBJECTIVE

The Mn-PB NPs prepared in this work have uniform particle size and show good performances both in MRI for T1 and T2 enhancement and in photothermal therapy in vitro without obvious cytotoxicity.