Multiparametric Magnetic Resonance Imaging (mpMRI) is the gold standard for diagnosing brain tumors, especially gliomas, which are difficult to segment due to their heterogeneity and varied sub-regions. While manual segmentation is time-consuming and error-prone, Deep Learning (DL) automates the process with greater accuracy and speed. We conducted ablation studies on surveyed articles to evaluate the impact of "add-on" modules-addressing challenges like spatial information loss, class imbalance, and overfitting-on glioma segmentation performance. Advanced modules-such as atrous (dilated) convolutions, inception, attention, transformer, and hybrid modules-significantly enhance segmentation accuracy, efficiency, multiscale feature extraction, and boundary delineation, while lightweight modules reduce computational complexity. Experiments on the Brain Tumor Segmentation (BraTS) dataset (comprising low- and high-grade gliomas) confirm their robustness, with top-performing models achieving high Dice score for tumor sub-regions. This survey underscores the need for optimal module selection and placement to balance speed, accuracy, and interpretability in glioma segmentation. Future work should focus on improving model interpretability, lowering computational costs, and boosting generalizability. Tools like NeuroQuant® and Raidionics demonstrate potential for clinical translation. Further refinement could enable regulatory approval, advancing precision in brain tumor diagnosis and treatment planning.

The test-time adaptation (TTA) of deep-learning-based semantic segmentation models, specific to individual patient data, was addressed in this study. The existing TTA methods in medical imaging are often unconstrained, require anatomical prior information or additional neural networks built during training phase, making them less practical, and prone to performance deterioration. In this study, a novel framework based on information geometric principles was proposed to achieve generic, off-the-shelf, regularized patient-specific adaptation of models during test-time. By considering the pre-trained model and the adapted models as part of statistical neuromanifolds, test-time adaptation was treated as constrained functional regularization using information geometric measures, leading to improved generalization and patient optimality. The efficacy of the proposed approach was shown on three challenging problems: 1) improving generalization of state-of-the-art models for segmenting COVID-19 anomalies in Computed Tomography (CT) images 2) cross-institutional brain tumor segmentation from magnetic resonance (MR) images, 3) segmentation of retinal layers in Optical Coherence Tomography (OCT) images. Further, it was demonstrated that robust patient-specific adaptation can be achieved without adding significant computational burden, making it first of its kind based on information geometric principles.

Placental volume measurements can potentially identify high-risk pregnancies. We aimed to develop and validate a new method for placental volume measurements using tracked 2D ultrasound and automatic image segmentation. We included 43 pregnancies at gestational week 27 and acquired placental images using a 2D ultrasound probe with position tracking, and trained a convolutional neural network (CNN) for automatic image segmentation. The automatically segmented 2D images were combined with tracking data to calculate placental volume. For 15 of the included pregnancies, placental volume was also estimated based on MRI examinations, 3D ultrasound and manually segmented 2D ultrasound images. The ultrasound methods were compared to MRI (gold standard). The CNN demonstrated good performance in automatic image segmentation (F1-score 0.84). The correlation with MRI-based placental volume was similar for tracked 2D ultrasound using automatically segmented images (absolute agreement intraclass correlation coefficient [ICC] 0.58, 95% CI 0.13-0.84) and manually segmented images (ICC 0.59, 95% CI 0.13-0.84). The 3D ultrasound method showed lower ICC (0.35, 95% CI -0.11 to 0.74) than the methods based on tracked 2D ultrasound. Tracked 2D ultrasound with automatic image segmentation is a promising new method for placental volume measurements and has potential for further improvement.

Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasound images, resulting in segmentation over-fitting to weakly annotated regions without precise delineation of target boundaries. We propose a dual-branch weakly-supervised learning framework to optimize the backbone segmentation network by calibrating semantic features into rational spatial distribution under the indirect, coarse guidance of the bounding box mask. Specifically, in the spatial arrangement consistency branch, the maximum activations sampled from the preliminary segmentation prediction and the bounding box mask along the horizontal and vertical dimensions are compared to measure the rationality of the approximate target localization. In the hierarchical prediction consistency branch, the target and background prototypes are encapsulated from the semantic features under the combined guidance of the preliminary segmentation prediction and the bounding box mask. The secondary segmentation prediction induced from the prototypes is compared with the preliminary prediction to quantify the rationality of the elaborated target and background semantic feature perception. Experiments on three thyroid datasets illustrate that our model outperforms existing weakly-supervised methods for thyroid gland and nodule segmentation and is comparable to the performance of fully-supervised methods with reduced annotation time. The proposed method has provided a weakly-supervised segmentation strategy by simultaneously considering the target's location and the rationality of target and background semantic features distribution. It can improve the applicability of deep learning based segmentation in the clinical practice.

Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. However, the lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to delineate precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this article, we present a scale-aware super-resolution (SR) network to adaptively segment lesions of various sizes from low-resolution (LR) medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask SR (LMSR) and lesion image SR (LISR). Meanwhile, we introduce scale-aware dilated convolution (SDC) blocks into the multitask decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution (HR) features, we propose a feature affinity (FA) module and a scale affinity (SA) module to enhance the multitask learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared with other state-of-the-art methods. Code will be available at: https://github.com/poiuohke/SASR_Net.

Magnetic resonance imaging (MRI), as a non-invasive detection method, is crucial for the clinical diagnosis and treatment plan of rectal cancer. However, due to the low contrast of rectal tumor signal in MRI, segmentation is often inaccurate. In this paper, we propose a new three-dimensional rectal tumor segmentation method CAU-Net based on T2-weighted MRI images. The method adopts a convolutional neural network to extract multi-scale features from MRI images and uses a Contour-Aware decoder and attention fusion block (AFB) for contour enhancement. We also introduce adversarial constraint to improve augmentation performance. Furthermore, we construct a dataset of 108 MRI-T2 volumes for the segmentation of locally advanced rectal cancer. Finally, CAU-Net achieved a DSC of 0.7112 and an ASD of 2.4707, which outperforms other state-of-the-art methods. Various experiments on this dataset show that CAU-Net has high accuracy and efficiency in rectal tumor segmentation. In summary, proposed method has important clinical application value and can provide important support for medical image analysis and clinical treatment of rectal cancer. With further development and application, this method has the potential to improve the accuracy of rectal cancer diagnosis and treatment.

The SAM (segment anything model) is a foundation model for general purpose image segmentation, however, when it comes to a specific medical application, such as segmentation of both ventricles from the 2D cardiac MRI, the results are not satisfactory. The scarcity of labeled medical image data further increases the difficulty to apply the SAM to medical image processing. To address these challenges, we propose the SAMBV by fine-tuning the SAM for semi-supervised segmentation of bi-ventricle from the 2D cardiac MRI. The SAM is tuned in three aspects, (i) the position and feature adapters are introduced so that the SAM can adapt to bi-ventricle segmentation. (ii) a dual-branch encoder is incorporated to collect missing local feature information in SAM so as to improve bi-ventricle segmentation. (iii) the interpolation consistency regularization (ICR) semi-supervised manner is utilized, allowing the SAMBV to achieve competitive performance with only 40% of the labeled data in the ACDC dataset. Experimental results demonstrate that the proposed SAMBV achieves an average Dice score improvement of 17.6% over the original SAM, raising its performance from 74.49% to 92.09%. Furthermore, the SAMBV outperforms other supervised SAM fine-tuning methods, showing its effectiveness in semi-supervised medical image segmentation tasks. Notably, the proposed method is specifically designed for 2D MRI data.

The management of skeletal-related events (SREs), particularly the prevention of pathological fractures, is crucial for cancer patients. Current clinical assessment of fracture risk is mostly based on medical images, but incorporating sequential images in the assessment remains challenging. This study addressed this issue by leveraging a comprehensive dataset consisting of 260 longitudinal micro-computed tomography (μCT) scans acquired in normal and breast cancer bearing mice. A machine learning (ML) model based on a spatial-temporal neural network was built to forecast bone structures from previous μCT scans, which were found to have an overall similarity coefficient (Dice) of 0.814 with ground truths. Despite the predicted lesion volumes (18.5 ​% ​± ​15.3 ​%) being underestimated by ∼21 ​% than the ground truths' (22.1 ​% ​± ​14.8 ​%), the time course of the lesion growth was better represented in the predicted images than the preceding scans (10.8 ​% ​± ​6.5 ​%). Under virtual biomechanical testing using finite element analysis (FEA), the predicted bone structures recapitulated the loading carrying behaviors of the ground truth structures with a positive correlation (y ​= ​0.863x) and a high coefficient of determination (R² ​= ​0.955). Interestingly, the compliances of the predicted and ground truth structures demonstrated nearly identical linear relationships with the lesion volumes. In summary, we have demonstrated that bone deterioration could be proficiently predicted using machine learning in our preclinical dataset, suggesting the importance of large longitudinal clinical imaging datasets in fracture risk assessment for cancer bone metastasis.

Accurate internal target volume (ITV) estimation is essential for effective and safe radiation therapy in liver cancer. This study evaluates the clinical value of an ultraquality 4-dimensional magnetic resonance imaging (UQ 4D-MRI) technique for ITV estimation. The UQ 4D-MRI technique maps motion information from a low spatial resolution dynamic volumetric MRI onto a high-resolution 3-dimensional MRI used for radiation treatment planning. It was validated using a motion phantom and data from 13 patients with liver cancer. ITV generated from UQ 4D-MRI (ITV_4D) was compared with those obtained through isotropic expansions (ITV_{2 mm} and ITV_{5 mm}) and those measured using conventional 4D-computed tomography (computed tomography-based ITV, ITV_CT) for each patient. Phantom studies showed a displacement measurement difference of <5% between UQ 4D-MRI and single-slice 2-dimensional cine MRI. In patient studies, the maximum superior-inferior displacements of the tumor on UQ 4D-MRI showed no significant difference compared with single-slice 2-dimensional cine imaging (<i>P</i> = .985). Computed tomography-based ITV showed no significant difference (<i>P</i> = .72) with ITV_4D, whereas ITV_{2 mm} and ITV_{5 mm} significantly overestimated the volume by 29.0% (<i>P</i> = .002) and 120.7% (<i>P</i> < .001) compared with ITV_4D, respectively. UQ 4D-MRI enables accurate motion assessment for liver tumors, facilitating precise ITV delineation for radiation treatment planning. Despite uncertainties from artificial intelligence-based delineation and variations in patients' respiratory patterns, UQ 4D-MRI excels at capturing tumor motion trajectories, potentially improving treatment planning accuracy and reducing margins in liver cancer radiation therapy.

VSBONE^® BSI (VSBONE), an automated bone scan index (BSI) measurement system was updated from version 2.1 (ver.2) to 3.0 (ver.3). VSBONE ver.3 incorporates deep learning of the skeletal structures of 957 new women, and it can be applied in patients with breast cancer. However, the performance of the updated VSBONE remains unclear. This study aimed to validate the diagnostic accuracy of the VSBONE system in patients with breast cancer. In total, 220 Japanese patients with breast cancer who underwent bone scintigraphy with single-photon emission computed tomography/computed tomography (SPECT/CT) were retrospectively analyzed. The patients were diagnosed with active bone metastases (<i>n</i> = 20) and non-bone metastases (<i>n</i> = 200) according to the physician's radiographic image interpretation. The patients were assessed using the VSBONE ver.2 and VSBONE ver.3, and the BSI findings were compared with the interpretation results by the physicians. The occurrence of segmentation errors, the association of BSI between VSBONE ver.2 and VSBONE ver.3, and the diagnostic accuracy of the systems were evaluated. VSBONE ver.2 and VSBONE ver.3 had segmentation errors in four and two patients. Significant positive linear correlations were confirmed in both versions of the BSI (<i>r</i> = 0.92). The diagnostic accuracy was 54.1% in VSBOBE ver.2, and 80.5% in VSBONE ver.3 <i>(P</i> < 0.001), respectively. The diagnostic accuracy of VSBONE was improved through deep learning of the female skeletal structures. The updated VSBONE ver.3 can be a reliable automated system for measuring BSI in patients with breast cancer.