Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall loss. To address this, instance-wise loss functions and metrics have been proposed to evaluate segmentation quality on a per-lesion basis. We introduce CC-DiceCE, a loss function based on the CC-Metrics framework, and compare it with the existing blob loss. Both are benchmarked against a DiceCE baseline within the nnU-Net framework, which provides a robust and standardized setup. We find that CC-DiceCE loss increases detection (recall) with minimal to no degradation in segmentation performance, though with dataset-dependent trade-offs in precision. Furthermore, our multi-dataset study shows that CC-DiceCE generally outperforms blob loss.
CT perfusion (CTP) has been used to triage ischemic stroke patients in the early stage, because of its speed, availability, and lack of contraindications. Perfusion parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) and time of peak (Tmax) could also be computed from CTP data. However, CTP data or the perfusion parameters, are ambiguous to locate the infarct core or tissue at risk (penumbra), which is normally confirmed by the follow-up Diffusion Weighted Imaging (DWI) or perfusion diffusion mismatch. In this paper, we propose a novel generativ…
Deep convolutional neural networks have driven substantial advancements in the automatic understanding of images. Requiring a large collection of images and their associated annotations is one of the main bottlenecks limiting the adoption of deep networks. In the task of medical image segmentation, requiring pixel-level semantic annotations performed by human experts exacerbate this difficulty. This paper proposes a new framework to train a fully convolutional segmentation network from a large set of cheap unreliable annotations and a small set of expert-level clean annotations. We propose a s…
A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学A.I.维尔塔宁分子科学研究所）；Charles River Discovery Services（查尔斯河发现服务公司）
We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and additional skip connections that, as we show in our experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR image preprocessing, such as bias-field correction or registration to a template, produces segmentations in less than a second, and its GPU memory r…
Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations
College of Physics and Information Engineering（物理与信息工程学院）；Fuzhou University（福州大学）；Neurosurgery Research Institute, The First Affiliated Hospital（第一附属医院神经外科研究所）；Fujian Medical University（福建医科大学）；National Regional Medical Center, Binhai Campus of the First Affiliated Hospital（第一附属医院滨海院区国家区域医疗中心）；Fujian Institute of Brain Disorders and Brain Science（福建脑疾病与脑科学研究所）；Fujian Clinical Research Center for Neurological Diseases（福建省神经疾病临床研究中心）；Fujian Provincial Clinical Research Center for Neurological Diseases, First Affiliated Hospital（第一附属医院福建省神经疾病临床研究中心）；Clinical Research and Translation Center, The First Affiliated Hospital（第一附属医院临床研究与转化中心）
Diffusion-Weighted Magnetic Resonance Imaging (DWI) is widely used for early cerebral infarct detection caused by ischemic stroke. Manual segmentation is done by a radiologist as a common clinical process, nonetheless, challenges of cerebral infarct segmentation come from low resolution and uncertain boundaries. Many segmentation techniques have been proposed and proved by manual segmentation as gold standard. In order to reduce human error in research operation and clinical process, we adopt a semi-automatic segmentation as gold standard using Fluid-Attenuated Inversion-Recovery (FLAIR) Magne…
Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settings
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希-亚历山大大学（FAU））；Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；German Centre for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心（DZNE））
University of Minho（米尼奥大学）；Bern University Hospital（伯尔尼大学医院）
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention. Treatment selection is influenced by clinical diagnosis, which requires delineating the stroke lesion, as well as characterising cerebral blood flow dynamics using neuroimaging acquisitions. Nonetheless, predicting the final stroke lesion is an intricate task, due to the variability in lesion size, shape, location and the underlying cerebral haemodynamic processe…
Image Sciences Institute, UMC Utrecht（乌得勒支大学医学中心影像科学研究所）
Cerebral microbleeds are small, dark, round lesions that can be visualised on T2*-weighted MRI or other sequences sensitive to susceptibility effects. In this work, we propose a multi-stage approach to both microbleed detection and segmentation. First, possible microbleed locations are detected with a Mask R-CNN technique. Second, at each possible microbleed location, a simple U-Net performs the final segmentation. This work used the 72 subjects as training data provided by the "Where is VALDO?" challenge of MICCAI 2021.
University of Pennsylvania（宾夕法尼亚大学）；University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；University of Bern（伯尔尼大学）；Weill Cornell Medical College（威尔康奈尔医学院）；Boston University（波士顿大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Washington（华盛顿大学）
Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenien…
FocusSDF: Boundary-Aware Learning for Medical Image Segmentation via Signed Distance Supervision
College of Innovation and Technology (CIT), University of Michigan-Flint（创新与技术学院（CIT），密歇根大学弗林特分校）；College of Information Technology (CIT), United Arab Emirates University（信息科技学院（CIT），阿联酋大学）；Department of Computer Science and Engineering, Oakland University（计算机科学与工程系，奥克兰大学）；Executive Vice-Chair at Department of Neurosurgery, Henry Ford Health System（神经外科部门执行副主席，亨利福特健康系统）
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Northeastern University（东北大学）；Caliber Imaging and Diagnostics（Caliber影像与诊断公司）；SUNY Downstate Medical Center（纽约州立大学下州医学中心）；Skin Medical Research Diagnostics, P.L.L.C.（皮肤医学研究诊断有限责任公司）；University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）
In-vivo optical microscopy is advancing into routine clinical practice for non-invasively guiding diagnosis and treatment of cancer and other diseases, and thus beginning to reduce the need for traditional biopsy. However, reading and analysis of the optical microscopic images are generally still qualitative, relying mainly on visual examination. Here we present an automated semantic segmentation method called "Multiscale Encoder-Decoder Network (MED-Net)" that provides pixel-wise labeling into classes of patterns in a quantitative manner. The novelty in our approach is the modeling of textura…
Santa Creu i Sant Pau Hospital（圣十字圣保罗医院）；Universitat Autonòma Barcelona（巴塞罗那自治大学）；Vrije University Medical Centre Amsterdam（阿姆斯特丹自由大学医学中心）
Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly …
University College London（伦敦大学学院）；King’s College London（伦敦国王学院）；Erasmus MC（伊拉斯谟医学中心）；Amsterdam University Medical Centre（阿姆斯特丹大学医学中心）
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the \textit{VAscular Lesions DetectiOn and Segmentation} (\textit{Where is VALDO?}) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (…
Cerebral microbleeds are small hypointense lesions visible on magnetic resonance imaging (MRI) with gradient echo, T2*, or susceptibility weighted (SWI) imaging. Assessment of cerebral microbleeds is mostly performed by visual inspection. The past decade has seen the rise of semi-automatic tools to assist with rating and more recently fully automatic tools for microbleed detection. In this work, we explore the use of nnU-Net as a fully automated tool for microbleed segmentation. Data was provided by the ``Where is VALDO?'' challenge of MICCAI 2021. The final method consists of nnU-Net in the `…
University of Eastern Finland（东芬兰大学）；Charles River Discovery Services（查尔斯河发现服务公司）；Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi（费米中心——恩里科·费米物理历史博物馆与研究中心）；Sapienza Università di Roma（罗马第一大学）
We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resembles an autoencoder and it incorporates residual blocks that facilitate its optimization. RatLesNetv2 is trained end to end on three-dimensional images and it requires no preprocessing. We evaluated RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat brain MRI scans of 671 rats at nine different lesion stages that were used to study focal cerebral ischemia for drug development. In addition, w…
Look-Closer-Then-Diagnose: Confidence-Aware Ultrasound VQA via Active Zooming
Computer Aided Medical Procedures (CAMP)（计算机辅助医疗程序）；TU Munich, Germany（慕尼黑工业大学，德国）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Munich, Germany（慕尼黑，德国）；Zhongshan Hospital, Fudan University, China（复旦大学中山医院）；The University of Hong Kong, Hongkong, China（香港大学，香港，中国）
AI Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学AI·维尔塔宁分子科学研究所）；Charles River Discovery Services（查尔斯河发现服务公司）；Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi（费米中心——恩里科·费米物理历史博物馆与研究中心）
Manual segmentation of rodent brain lesions from magnetic resonance images (MRIs) is an arduous, time-consuming and subjective task that is highly important in pre-clinical research. Several automatic methods have been developed for different human brain MRI segmentation, but little research has targeted automatic rodent lesion segmentation. The existing tools for performing automatic lesion segmentation in rodents are constrained by strict assumptions about the data. Deep learning has been successfully used for medical image segmentation. However, there has not been any deep learning approach…
Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI
