Automatic segmentation of kidney and kidney tumour in Computed Tomography (CT) images is essential, as it uses less time as compared to the current gold standard of manual segmentation. However, many hospitals are still reliant on manual study and segmentation of CT images by medical practitioners because of its higher accuracy. Thus, this study focuses on the development of an approach for automatic kidney and kidney tumour segmentation in contrast-enhanced CT images. A method based on Convolutional Neural Network (CNN) was proposed, where a 3D U-Net segmentation model was developed and trained to delineate the kidney and kidney tumour from CT scans. Each CT image was pre-processed before inputting to the CNN, and the effect of down-sampled and patch-wise input images on the model performance was analysed. The proposed method was evaluated on the publicly available 2021 Kidney and Kidney Tumour Segmentation Challenge (KiTS21) dataset. The method with the best performing model recorded an average training Dice score of 0.6129, with the kidney and kidney tumour Dice scores of 0.7923 and 0.4344, respectively. For testing, the model obtained a kidney Dice score of 0.8034, and a kidney tumour Dice score of 0.4713, with an average Dice score of 0.6374.

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.

自动化的腹部多器官分割是计算机辅助诊断腹部器官相关疾病的至关重要但具有挑战性的任务。尽管许多深度学习模型在许多医学图像分割任务中取得了显着的成功，但由于腹部器官的不同大小以及它们之间的含糊界限，腹部器官的准确分割仍然具有挑战性。在本文中，我们提出了一个边界感知网络（BA-NET），以分段CT扫描和MRI扫描进行腹部器官。该模型包含共享编码器，边界解码器和分割解码器。两个解码器都采用了多尺度的深度监督策略，这可以减轻可变器官尺寸引起的问题。边界解码器在每个量表上产生的边界概率图被用作提高分割特征图的注意。我们评估了腹部多器官细分（AMOS）挑战数据集的BA-NET，并获得了CT扫描的多器官分割的平均骰子分数为89.29 $ \％$，平均骰子得分为71.92 $ \％$ \％$ \％ MRI扫描。结果表明，在两个分割任务上，BA-NET优于NNUNET。

在这项工作中，我们介绍了我们提出的方法，该方法是使用SWIN UNETR和基于U-NET的深神经网络体系结构从CT扫描中分割肺动脉的方法。六个型号，基于SWIN UNETR的三个型号以及基于3D U-NET的三个模型，使用加权平均值来制作最终的分割掩码。我们的团队通过这种方法获得了84.36％的多级骰子得分。我们的工作代码可在以下链接上提供：https：//github.com/akansh12/parse2022。这项工作是Miccai Parse 2022挑战的一部分。

目的：要开发和验证计算机工具，用于在计算机断层扫描（CT）扫描上描绘的上述组织的自动和同时分割的计算机工具：内脏脂肪（VAT），皮下脂肪（SAT），骨骼脂肪（IMAT），骨骼肌（SM）和骨头。方法：使用了从癌症成像档案（TCIA）获得的100 CT扫描的队列 - 50个全身正电子发射断层扫描（PET）-CTS，25胸和25腹部。手动注释五种不同的身体组合物（VAT，SAT，IMAT，SM和骨骼）。培训次训练策略用于效率。使用已经注释的案例训练了UNET模型。然后，该模型用于为剩余情况启用半自动注释。使用10倍的交叉验证方法来开发和验证几种卷积神经网络（CNNS）的性能，包括UNET，复发性残留的UNET（R2UNET）和UNET ++。在培训CNN模型时使用3-D贴片采样操作。测试了单独培训的CNN模型，看看它们是否可以达到更好的性能而不是共同分割它们。配对样品T检验用于测试统计显着性。结果：在三种CNN模型中，UNET在共同分割五个身体组合物中表现出最佳的整体性能，骰子系数为0.840 +/- 0.091,0.908 +/- 0.067,0.603 +/- 0.084,0.889 +/- 0.027，和0.884 +/- 0.031，Jaccard指数为0.734 +/- 0.119，0.837 +/- 0.096,0.437 +/- 0.082,0.800 +/- 0.042,0.793 +/- 0.049，分别用于增值税，SAT，IMAT， SM和骨头。结论：分段体组合物中的CNN模型中没有显着差异，但共同分段体组合物比分别分割更好的性能。

CT灌注（CTP）是一项体检，用于测量对比度溶液通过像素逐像素的大脑通过大脑的通过。目的是为缺血性病变迅速绘制“灌注图”（即脑血体积，脑血流量和峰值的时间），并能够区分核心和甲瘤区域。在缺血性中风的背景下，精确而快速的诊断可以确定脑组织的命运，并在紧急情况下指导干预和治疗。在这项工作中，我们介绍了UnitObrain数据集，这是CTP的第一个开源数据集。它包括一百多名患者的队列，并伴随着患者元数据和最新算法获得的地面真相图。我们还建议使用欧洲图书馆ECVL和EDDL进行图像处理和开发深度学习模型，提出了一种基于神经网络的新型算法。神经网络模型获得的结果与地面真相相匹配，并为所需数量的CT地图的潜在子采样开辟了道路，这对患者施加了重辐射剂量。

Quantitative cancer image analysis relies on the accurate delineation of tumours, a very specialised and time-consuming task. For this reason, methods for automated segmentation of tumours in medical imaging have been extensively developed in recent years, being Computed Tomography one of the most popular imaging modalities explored. However, the large amount of 3D voxels in a typical scan is prohibitive for the entire volume to be analysed at once in conventional hardware. To overcome this issue, the processes of downsampling and/or resampling are generally implemented when using traditional convolutional neural networks in medical imaging. In this paper, we propose a new methodology that introduces a process of sparsification of the input images and submanifold sparse convolutional networks as an alternative to downsampling. As a proof of concept, we applied this new methodology to Computed Tomography images of renal cancer patients, obtaining performances of segmentations of kidneys and tumours competitive with previous methods (~84.6% Dice similarity coefficient), while achieving a significant improvement in computation time (2-3 min per training epoch).

自动分割方法是医学图像分析的重要进步。特别是机器学习技术和深度神经网络，是最先进的大多数医学图像分割任务。类别不平衡的问题在医疗数据集中构成了重大挑战，病变通常占据相对于背景的相对于较小的体积。深度学习算法培训中使用的损失函数对类别不平衡的鲁棒性不同，具有模型收敛的直接后果。分割最常用的损耗函数基于交叉熵损耗，骰子丢失或两者的组合。我们提出了统一的联络损失，是一种新的分层框架，它概括了骰子和基于跨熵的损失，用于处理类别不平衡。我们评估五个公共可用的损失功能，类不平衡的医学成像数据集：CVC-ClinicDB，船舶提取数字视网膜图像（驱动器），乳房超声波2017（Bus2017），脑肿瘤分割2020（Brats20）和肾肿瘤分割2019 （套件19）。我们将损耗功能性能与六个骰子或基于跨熵的损耗函数进行比较，横跨二进制二进制，3D二进制和3D多包子分段任务，展示我们所提出的损失函数对类不平衡具有强大，并且始终如一地优于其他丢失功能。源代码可用：https://github.com/mlyg/unified-focal-loss

胸部计算机断层扫描的气道分割在肺部疾病诊断中起着至关重要的作用。与手动分割相比，基于U-NET体系结构的计算机辅助气道分割更有效，更准确。在本文中，我们采用了由骰子损失功能训练的U $^2 $ -NET，以基于ATM'22提供的299次培训CT扫描，对多站点CT扫描的气道树进行建模。从训练中将派生的显着性概率图应用于验证数据以提取相应的气道树。该观察结果表明，大多数分割的气道树从准确性和连通性的角度表现出色。将诸如非航空区域标签和去除之类的改进应用于某些获得的气道树模型，以显示二进制结果的最大组成部分。

Brain tumor imaging has been part of the clinical routine for many years to perform non-invasive detection and grading of tumors. Tumor segmentation is a crucial step for managing primary brain tumors because it allows a volumetric analysis to have a longitudinal follow-up of tumor growth or shrinkage to monitor disease progression and therapy response. In addition, it facilitates further quantitative analysis such as radiomics. Deep learning models, in particular CNNs, have been a methodology of choice in many applications of medical image analysis including brain tumor segmentation. In this study, we investigated the main design aspects of CNN models for the specific task of MRI-based brain tumor segmentation. Two commonly used CNN architectures (i.e. DeepMedic and U-Net) were used to evaluate the impact of the essential parameters such as learning rate, batch size, loss function, and optimizer. The performance of CNN models using different configurations was assessed with the BraTS 2018 dataset to determine the most performant model. Then, the generalization ability of the model was assessed using our in-house dataset. For all experiments, U-Net achieved a higher DSC compared to the DeepMedic. However, the difference was only statistically significant for whole tumor segmentation using FLAIR sequence data and tumor core segmentation using T1w sequence data. Adam and SGD both with the initial learning rate set to 0.001 provided the highest segmentation DSC when training the CNN model using U-Net and DeepMedic architectures, respectively. No significant difference was observed when using different normalization approaches. In terms of loss functions, a weighted combination of soft Dice and cross-entropy loss with the weighting term set to 0.5 resulted in an improved segmentation performance and training stability for both DeepMedic and U-Net models.

机器学习算法支撑现代诊断辅助软件，这在临床实践中证明了有价值的，特别是放射学。然而，不准确的是，主要是由于临床样本的可用性有限，用于培训这些算法，妨碍他们在临床医生中更广泛的适用性，接受和识别。我们对最先进的自动质量控制（QC）方法进行了分析，可以在这些算法中实现，以估计其输出的确定性。我们验证了识别磁共振成像数据中的白质超收缩性（WMH）的大脑图像分割任务上最有前途的方法。 WMH是在上层前期成年中常见的小血管疾病的关联，并且由于其变化的尺寸和分布模式而尤其具有挑战性。我们的研究结果表明，不确定度和骰子预测的聚集在此任务的故障检测中最有效。两种方法在0.82至0.84的情况下独立改善平均骰子。我们的工作揭示了QC方法如何有助于检测失败的分割案例，从而使自动分割更可靠，适合临床实践。

为了促进医学图像分割技术的开发，提供了用于多功能医疗图像分割的大型腹部多器官数据集Amos，并通过使用数据集来构成AMOS 2022挑战。在本报告中，我们介绍了AMOS 2022挑战的解决方案。我们采用具有深远视觉的剩余U-NET作为我们的基本模型。实验结果表明，对于仅CT任务和CT/MRI任务，骰子相似系数和归一化表面骰子的平均得分分别为0.8504和0.8476。

肾脏结构细分是计算机辅助诊断基于手术的肾癌的至关重要但具有挑战性的任务。尽管许多深度学习模型在许多医学图像分割任务中取得了显着的成功，但由于肾脏肿瘤的尺寸可变，肾脏肿瘤及其周围环境之间的歧义范围可变，因此对计算机层析造影血管造影（CTA）图像的肾脏结构的准确分割仍然具有挑战性。 。在本文中，我们在CTA扫描中提出了一个边界感知网络（BA-NET），以分段肾脏，肾脏肿瘤，动脉和静脉。该模型包含共享编码器，边界解码器和分割解码器。两个解码器都采用了多尺度的深度监督策略，这可以减轻肿瘤大小可变的问题。边界解码器在每个量表上产生的边界概率图被用作提高分割特征图的注意。我们在肾脏解析（KIPA）挑战数据集上评估了BA-NET，并通过使用4倍的交叉验证来实现CTA扫描的肾脏结构细分的平均骰子得分为89.65 $ \％$。结果证明了BA-NET的有效性。

互动细分最近引起了专业任务的关注，需要专家输入来进一步提高分割性能。在这项工作中，我们提出了一种新颖的交互式分割框架，其中用户点击基于当前分割掩码的大小动态地调整。点击区域形成重量映射，并作为一种新的加权损失函数馈送到深度神经网络。为了评估我们的损失函数，采用了应用前景和背景用户的交互式U-Net（IU-Net）模型作为主要交互方法。我们在BCV数据集上培训并验证，同时从MSD数据集测试脾脏和结肠癌CT图像，与使用我们的加权损耗功能的标准U-Net相比，改善整体分割精度。应用动态用户点击大小通过仅利用单个用户交互，分别将整体准确性增加5.60％和10.39％。

Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively. The docker image for the winning submission is publicly available at (https://hub.docker.com/r/razeineldin/camed22).

辐射肿瘤学家的工作是传递指向肿瘤的X射线梁，同时避免胃和肠。使用MR-LINACS（磁共振成像和线性加速器系统），肿瘤学家可以可视化肿瘤的位置，并根据肿瘤细胞的存在允许精确剂量，这种肿瘤细胞的存在可能每天变化。当前概述胃和肠的位置以调节X射线束方向以避免器官的剂量递送到肿瘤。这是一个耗时且劳动密集型的过程，除非深度学习方法可以自动化细分过程，否则可以轻松地将15分钟的治疗方法延长至一小时。本文讨论了使用深度学习的自动分割过程，以使该过程更快，并允许更多患者获得有效的治疗。

Segmentation of lung tissue in computed tomography (CT) images is a precursor to most pulmonary image analysis applications. Semantic segmentation methods using deep learning have exhibited top-tier performance in recent years. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3D) pulmonary CT images, which we call it Lung-Net. We conjectured that a significant deeper network with inceptionV3 units can achieve a better feature representation of lung CT images without increasing the model complexity in terms of the number of trainable parameters. The method has three main advantages. First, a U-Net architecture with InceptionV3 blocks is developed to resolve the problem of performance degradation and parameter overload. Then, using information from consecutive slices, a new data structure is created to increase generalization potential, allowing more discriminating features to be extracted by making data representation as efficient as possible. Finally, the robustness of the proposed segmentation framework was quantitatively assessed using one public database to train and test the model (LUNA16) and two public databases (ISBI VESSEL12 challenge and CRPF dataset) only for testing the model; each database consists of 700, 23, and 40 CT images, respectively, that were acquired with a different scanner and protocol. Based on the experimental results, the proposed method achieved competitive results over the existing techniques with Dice coefficient of 99.7, 99.1, and 98.8 for LUNA16, VESSEL12, and CRPF datasets, respectively. For segmenting lung tissue in CT images, the proposed model is efficient in terms of time and parameters and outperforms other state-of-the-art methods. Additionally, this model is publicly accessible via a graphical user interface.

Glioblastomas是最具侵略性的快速生长的主要脑癌，起源于大脑的胶质细胞。准确鉴定恶性脑肿瘤及其子区域仍然是医学图像分割中最具挑战性问题之一。脑肿瘤分割挑战（Brats）是自动脑胶质细胞瘤分割算法的流行基准，自于其启动。在今年的挑战中，Brats 2021提供了2,000名术前患者的最大多参数（MPMRI）数据集。在本文中，我们提出了两个深度学习框架的新聚合，即在术前MPMRI中的自动胶质母细胞瘤识别的Deepseg和NNU-Net。我们的集合方法获得了92.00,87.33和84.10和Hausdorff距离为3.81,8.91和16.02的骰子相似度分数，用于增强肿瘤，肿瘤核心和全肿瘤区域，单独进行。这些实验结果提供了证据表明它可以在临床上容易地应用，从而助攻脑癌预后，治疗计划和治疗反应监测。

肝脏是脊椎动物中最关键的代谢器官之一，由于其在人体中的重要功能，例如废物产物和药物的血液排毒。由于肝肿瘤引起的肝病是全球最常见的死亡率之一。因此，在肿瘤发育的早期阶段检测肝肿瘤是医疗治疗的关键部分。许多成像方式可以用作检测肝肿瘤的帮助工具。计算机断层扫描（CT）是软组织器官（例如肝脏）最常用的成像方式。这是因为它是一种侵入性方式，可以相对迅速捕获。本文提出了一个有效的自动肝分割框架，以使用3D CNN深度元网络模型检测和分割肝脏腹部扫描。许多研究采用了精确分割肝区域，然后使用分割的肝区域作为肿瘤分割方法的输入，因为它降低了由于将腹部器官分割为肿瘤而导致的错误率。所提出的3D CNN DeepMedic模型具有两个输入途径，而不是一个途径，如原始3D CNN模型所示。在本文中，该网络提供了多个腹部CT版本，这有助于提高细分质量。提出的模型分别达到94.36％，94.57％，91.86％和93.14％的精度，灵敏度，特异性和骰子相似性得分。实验结果表明该方法的适用性。

域适应（DA）最近在医学影像社区提出了强烈的兴趣。虽然已经提出了大量DA技术进行了用于图像分割，但大多数这些技术已经在私有数据集或小公共可用数据集上验证。此外，这些数据集主要解决了单级问题。为了解决这些限制，与第24届医学图像计算和计算机辅助干预（Miccai 2021）结合第24届国际会议组织交叉模态域适应（Crossmoda）挑战。 Crossmoda是无监督跨型号DA的第一个大型和多级基准。挑战的目标是分割参与前庭施瓦新瘤（VS）的后续和治疗规划的两个关键脑结构：VS和Cochleas。目前，使用对比度增强的T1（CET1）MRI进行VS患者的诊断和监测。然而，使用诸如高分辨率T2（HRT2）MRI的非对比度序列越来越感兴趣。因此，我们创建了一个无人监督的跨模型分段基准。训练集提供注释CET1（n = 105）和未配对的非注释的HRT2（n = 105）。目的是在测试集中提供的HRT2上自动对HRT2进行单侧VS和双侧耳蜗分割（n = 137）。共有16支球队提交了评估阶段的算法。顶级履行团队达成的表现水平非常高（最佳中位数骰子 - vs：88.4％; Cochleas：85.7％）并接近完全监督（中位数骰子 - vs：92.5％;耳蜗：87.7％）。所有顶级执行方法都使用图像到图像转换方法将源域图像转换为伪目标域图像。然后使用这些生成的图像和为源图像提供的手动注释进行培训分割网络。