BACKGROUND: Vestibular schwannomas (VSs) are benign tumors often monitored over time, with measurement techniques for assessing growth rates subject to significant interobserver variability. Automatic segmentation of these tumors could provide a more reliable and efficient for tracking their progression, especially given the irregular shape and growth patterns of VS.
METHODS: Various studies and segmentation techniques employing different Convolutional Neural Network architectures and models, such as U-Net and convolutional-attention transformer segmentation, were analyzed. Models were evaluated based on their performance across diverse datasets, and challenges, including domain shift and data sharing, were scrutinized.
RESULTS: Automatic segmentation methods offer a promising alternative to conventional measurement techniques, offering potential benefits in precision and efficiency. However, these methods are not without challenges, notably the \"domain shift\" that occurs when models trained on specific datasets underperform when applied to different datasets. Techniques such as domain adaptation, domain generalization, and data diversity were discussed as potential solutions.
CONCLUSIONS: Accurate measurement of VS growth is a complex process, with volumetric analysis currently appearing more reliable than linear measurements. Automatic segmentation, despite its challenges, offers a promising avenue for future investigation. Robust well-generalized models could potentially improve the efficiency of tracking tumor growth, thereby augmenting clinical decision-making. Further work needs to be done to develop more robust models, address the domain shift, and enable secure data sharing for wider applicability.

Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets-the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.

Magnetic resonance imaging (MRI) has proved to be an invaluable component of pathogenesis research in osteoarthritis. Nevertheless, the detection of a meniscal lesion from magnetic resonance (MR) images is always challenging for both clinicians and researchers, because the surrounding tissues lead to similar signals within MR measurements, thus being difficult to discriminate. Moreover, the size and shape of osteoarthritic and non-osteoarthritic menisci vary to a large extent between individuals of same features, e.g. height, weight, age, etc. An effective way to visualize the entire volume of knee menisci is to segment the menisci voxels from the MR images, which is also useful to evaluate particular properties quantitatively. However, segmentation is a tedious and time-consuming task, and requires adequate training for being done properly. With the advancement of both MRI technology and computer methods, researchers have developed several algorithms to automate the task of meniscus segmentation of the individual knee during the last two decades. The objective of this systematic review was to present available fully automatic and semi-automatic segmentation methods of the knee meniscus published in different scientific articles according to the PRISMA statement. This review should provide a vivid description of the scientific advancements to clinicians and researchers in this field to help developing novel automated methods for clinical applications.

UNASSIGNED: To review the literature on auto-contouring methods of lung tumour volumes on four-dimensional computed tomography (4DCT).
UNASSIGNED: Manual delineation of lung tumour on 4DCT has been the gold standard in clinical practice. However, it is resource intensive due to the high volume of data which results in longer contouring duration and uncertainties in defining target. Auto-contouring may present as an attractive alternative by decreasing manual inputs required, thus improving the contouring process. This review aims to assess the accuracy, variability and contouring duration of automatic contouring compared with manual contouring in lung cancer on 4DCT datasets.
UNASSIGNED: A search and review of literature were conducted to identify studies regarding lung tumour contouring on 4DCT. Manual and auto-contours were assessed and compared based on accuracy, variability and contouring duration.
UNASSIGNED: Thirteen studies were included in this review and their results were compared. Accuracy of auto-contours was found to be comparable to manual contours. Auto-contouring resulted in lesser inter-observer variation when compared to manual contouring, however there was no significant reduction in intra-observer variability. Additionally, contouring duration was reduced with auto-contouring although long computation time could present as a bottleneck.
UNASSIGNED: Auto-contouring is reliable and efficient, producing accurate contours with better consistency compared to manual contours. However, manual inputs would still be required both before and after auto-propagation.

Liver cancer is a common type of malignant tumor in digestive system. At present, computed tomography (CT) plays an important role in the diagnosis and treatment of liver cancer. Segmentation of tumor lesions based on CT is thus critical in clinical diagnosis and treatment. Due to the limitations of manual segmentation, such as inefficiency and subjectivity, the automatic and accurate segmentation based on advanced computational techniques is becoming more and more popular. In this review, we summarize the research progress of automatic segmentation of liver cancer lesions based on CT scans. By comparing and analyzing the results of experiments, this review evaluate various methods objectively, so that researchers in related fields can better understand the current research progress of liver cancer segmentation based on CT scans.
肝癌是一种常见的消化系统恶性肿瘤。目前电子计算机断层扫描成像（CT）技术已在肝癌诊疗方面发挥着重要作用，而基于 CT 图像的肝癌病灶分割也在临床诊疗中扮演着重要角色。由于人工分割可能存在效率低、主观性强等缺点，因此利用电子计算机来实现对 CT 图像中肝癌病灶的准确、自动分割是当前的研究热点。本文就基于 CT 图像的肝癌病灶自动分割的进展予以综述，通过对比分析实验结果，评估各种分割方法，以便相关领域的科研工作者更好地了解目前肝癌 CT 分割方法的研究进展。.