Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

The objective of developing this software is to achieve auto-segmentation and tissue characterization. Therefore, the present algorithm has been designed and developed for analysis of medical images based on hybridization of syntactic and statistical approaches, using artificial neural network (ANN). This algorithm performs segmentation and classification as is done in human vision system, which recognizes objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness. The analysis of medical image is directly based on four steps: 1) image filtering, 2) segmentation, 3) feature extraction, and 4) analysis of extracted features by pattern recognition system or classifier. In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features with ANN as segmentation and classifier tool. The present approach directly combines second, third, and fourth steps into one algorithm. This is a semisupervised approach in which supervision is involved only at the level of defining texture-primitive cell; afterwards, algorithm itself scans the whole image and performs the segmentation and classification in unsupervised mode. The algorithm was first tested on Markov textures, and the success rate achieved in classification was 100%; further, the algorithm was able to give results on the test images impregnated with distorted Markov texture cell. In addition to this, the output also indicated the level of distortion in distorted Markov texture cell as compared to standard Markov texture cell. Finally, algorithm was applied to selected medical images for segmentation and classification. Results were in agreement with those with manual segmentation and were clinically correlated.

Brachytherapy applicators have come a long way since Danlos developed early intracavitary applicators to treat cervical cancer patients. Therefore, this review will help in the neoteric designs of intracavitary applicators. A detailed literature survey of the gynecological brachytherapy applicators from the era of preloading to conceptual intensity-modulated brachytherapy applicators has been carried out. Depending on the extent of the disease and patient anatomy, the selection of brachytherapy applicators plays a pivotal role in the treatment of cervical cancer. Furthermore, the selection of the applicators is also based on the imaging modalities to be used for applicator reconstruction and treatment planning. Dose acceleration in the target and reduction in nearby organs at risk can be optimized using an applicator having the capabilities of intensity-modulated brachytherapy. Now, three-dimensional printed applicators are used for patient-specific tailor-made treatment and they are fast replacing the old conventional applicators. Newer advancements in technology have greatly influenced the neoteric designs of intracavitary brachytherapy applicators.

The challenges of conventional animal models and two-dimensional (2D) in vitro cell cultures in effectively forecasting human toxicity have prompted a significant shift towards New Approach Methodologies (NAMs). This development centers on advanced humanized in vitro co-culture models that offer improved physiological relevance for toxicological assessment. This perspective highlights the critical role of biomaterials in the creation of complex microenvironments. This study demonstrates how biomaterials effectively mimic the original extracellular matrix (ECM) through controlled compositional, structural, mechanical, and biochemical signals, thereby enabling the development of sophisticated 3D spheroids, organoids, and Organ-on-Chip systems. These biomaterial-enhanced platforms are essential for precise evaluation of immunotoxicity, as they promote human-specific immune responses and targeted immunomodulation, and for carcinogenicity, as they accurately replicate the tumor microenvironment, affect cancer cell behavior, and enable patient-derived models. Moreover, we underscore the synergistic amalgamation of these biomaterial-based models with omics technologies and computational methodologies (QSAR, AI/ML) for thorough molecular insights and rational design. Despite ongoing challenges in standardization and high-throughput compatibility, the strategic utilization of biomaterials is set to transform predictive toxicology, expedite drug discovery, and promote personalized medicine, thereby diminishing dependence on animal testing and improving human safety.