Bhavya Ajani
Fast and automatic algorithm to segment Brain (intracranial region) from computed tomography (CT) head images using combination of HU thresholding, identification of intracranial voxels through ray intersection with cranium, special binary erosion and connected components per slice.
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Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
提出SemanticVessel数据集，利用4D-CTA动态扫描生成动静脉分割，通过强度引导区域生长标注20种动脉类别，并复用标签扩展数据集，训练模型提升精细分割性能。
Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of real world evaluation is best illustrated by case studies that have documented successes and failures in the translation of these models into clinical environments. A key prerequisite for the clinical adoption of these technologies is demonstrating generalizable ML model performance under real world circumstances. The purpose of this study was to demo…
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network. Methods: In this retrospective study, we evaluated 1,216 patients from two separate institutions who underwent CT for the presence of saccular IA>=2.5 mm. A two-step model was implemented: a 3D region proposal network for initial aneurysm detection and 3D DenseNetsfor false-pos…
Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each sl…
Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing strong supervision on what constitutes positive and negative samples. This constitutes a major obstacle for the development of DL models in radiology--in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)--where labels must come from manual annotations by expert radiologists at the image or slice-level. The…
Calibrated Self-supervised Vision Transformers Improve Intracranial Arterial Calcification Segmentation from Clinical CT Head Scans
针对3D医学图像分割中ViT应用受限问题，提出经MAE预训练的校准自监督ViT用于颅内动脉钙化分割，在IST-3数据上超越nnU-Net基线，提升层厚鲁棒性与临床风险分类效果。
We propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on computed tomography (CT) scans. The CNN plays the role of a slice-wise feature extractor while the LSTM is responsible for linking the features across slices. The whole architecture is trained end-to-end with input being an RGB-like image formed by stacking 3 different viewing windows of a single slice. We validate the method on the recent RSNA Intracranial Hemorrhage Detection challenge and on the CQ500 dataset. For t…
All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images
针对颅内出血CT图像同型异形、异型易混的分割难题，提出编码器用通道注意力、解码器结合空间与通道注意力的全注意力U-Net，较基线模型ResNet50+U-Net最高提升31.8%性能。
Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN architecture for the stroke identification problem using non-contrast CT. We applied the proposed DL model to historical patient data and also conducted clinical experiments involving ten experienced radiologists. Our model achieved strong results on historical data, and significantly outperformed seven radiologist out of ten, while being on par with the remaining three.
The optic nerve head represents the intraocular section of the optic nerve (ONH), which is prone to damage by intraocular pressure. The advent of optical coherence tomography (OCT) has enabled the evaluation of novel optic nerve head parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane opening minimum-rim-width, these seem to be promising optic nerve head parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these optical coherence tomography derived biomarkers are mostly extracted through manual segment…
A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images
针对头颈癌CT图像中包膜外侵犯（ECE）识别需手动标注淋巴结区域的问题，提出梯度映射引导可解释网络（GMGENet），利用Grad-CAM自动聚焦相关区域，无需标注，测试准确率90.2%，AUC达91.1%。
Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping
本研究比较零样本多模态大语言模型与传统深度学习在CT颅内出血亚型分类中的性能，发现传统模型全面优于MLLMs，但MLLMs通过语言交互增强可解释性，具有医学影像分析潜力。
Identification of Hemorrhage and Infarct Lesions on Brain CT Images using Deep Learning
本文评估了一种基于深度学习的算法，用于从头部NCCT扫描中识别颅内出血和梗死，并探讨其在大型医疗机构常规工作流程中的应用潜力与局限性。
Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies. In this work, we contributed an automated approach for Head and Neck (H&N) primary tumor segmentation in combined positron emission tomography / computed tomography (PET/CT) images in the context of the MICCAI 2020 Head and Neck Tumor segmentation challenge (HECKTOR). Our model was designed on the U-Net architecture with residual layers and supplemented with Squeeze-and-Excitation Normalization. The described method achieved competitive results in cro…
Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection
本研究将Mondrian保形预测应用于颅内出血检测，构建不确定性感知深度学习模型，在CQ500数据集上以99.7%准确率识别挑战性案例且不误标确定案例，验证了保形预测提升放射学深度学习可信赖性的有效性。
Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning applications have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary s…
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm was designed and trained to digitally stain (i.e. highlight) 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall dice coefficient (mean of all tissues) was $0.91 \…
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended t…
The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize classifier training for optic nerve head detection and localization in optical coherence tomography en face images of the retina. We examine the effect of domain knowledge driven spatial complexity reduction on the resulting optic nerve head segmentation and localization performance. We present a machine learning approach for segmenting optic nerve head in 2D en face projections of 3D widefield swept source optical co…
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
本文首次提出利用相邻切片深度描述符训练切片级分类器，在RSNA颅内出血挑战赛中单模型进入前4%，并在CQ500上显著优于基线，为三维医学影像诊断提供了高效通用的二维学习方法。
Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence
本文面向头颈癌诊断与预后，比较ViT与CNN分割模型并提出3D数据的2D处理视角，同时构建预后集成模型及可联合预测结局与分割肿瘤的ViT框架。
Automated Distinction of Intimal and Medial Intracranial Arterial Calcification from CT Head
本研究提出三种自动化方法区分颅内动脉钙化的内膜与中膜亚型，基于形状嵌入的方法性能最佳，加权F1达71.5，证明全自动量化可行且稳健。
Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma. Although several deep learning (DL) techniques have been recently proposed for the automated extraction (segmentation) and quantification of these morphological changes, the device specific nature and the difficulty in preparing manual segmentations (training data) limit their clinical adoption. With several new manufacturers and next-generation OCT devices entering the market, t…
Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images
本文提出基于 Resnet-LSTM 类激活图和无监督伪掩码细化的弱监督 ICH 分割方法，仅用图像级标签，在更小训练集上达到 0.55 Dice。
3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography
本文提出FM-CT模型，通过自监督学习在无需人工标注的情况下训练，提升头部CT图像中疾病检测的泛化能力，验证了自监督学习在医学影像中的有效性。
Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaus…
Purpose: (1) To develop a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field optical coherence tomography (OCT) scans; (2) To assess whether 3D macula or ONH structures (or the combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study was performed which included wide-field swept-source OCT scans from 319 glaucoma subjects and 298 non-glaucoma subjects. All scans were compensated to improve deep-tissue visibility. We developed a deep learning algorithm to automatically lab…
Intracranial hemorrhages (ICHs) are life-threatening brain injures with a relatively high incidence. In this paper, the automatic algorithm for the detection and classification of ICHs, including localization, is present. The set of binary convolutional neural network-based classifiers with a designed cascade-parallel architecture is used. This automatic system may lead to a distinct decrease in the diagnostic process's duration in acute cases. An average Jaccard coefficient of 53.7 % is achieved on the data from the publicly available head CT dataset CQ500.
Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
本研究通过基准测试五种特征提取器、七种分类头和三种分割策略，评估基础模型与放射组学在CT肺癌表型分析中的性能，发现最优设计取决于任务，推荐Curia结合肿瘤分割和CatBoost分类头作为安全默认方案。
Pre-processing and quality control of large clinical CT head datasets for intracranial arterial calcification segmentation
本文针对临床CT头部数据中颅内动脉钙化分割问题，提出结合模板配准与基于z轴信息及结构相似性度量的质量控制流程，在IST-3大规模数据集上实现高效感兴趣区域定位。
Brain CT has become a standard imaging tool for emergent evaluation of brain condition, and measurement of midline shift (MLS) is one of the most important features to address for brain CT assessment. We present a simple method to estimate MLS and propose a new alternative parameter to MLS: the ratio of MLS over the maximal width of intracranial region (MLS/ICWMAX). Three neurosurgeons and our automated system were asked to measure MLS and MLS/ICWMAX in the same sets of axial CT images obtained from 41 patients admitted to ICU under neurosurgical service. A weighted midline (WML) was plotted b…
Proton computed tomography (pCT) is a novel imaging modality developed for patients receiving proton radiation therapy. The purpose of this work was to investigate hull-detection algorithms used for preconditioning of the large and sparse linear system of equations that needs to be solved for pCT image reconstruction. The hull-detection algorithms investigated here included silhouette/space carving (SC), modified silhouette/space carving (MSC), and space modeling (SM). Each was compared to the cone-beam version of filtered backprojection (FBP) used for hull-detection. Data for testing these al…
An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of View
针对牙科CBCT截断视野导致的图像质量下降问题，提出一种基于隐式神经表示生成先验图像并校正投影差异的两阶段迭代重建方法，有效抑制截断伪影并提升图像质量。
Vision Foundation Models for Computed Tomography
本研究开发了CT-FM，一种基于148,000次CT扫描预训练的三维视觉基础模型，在分割、分诊、检索和语义理解任务上优于现有模型，并开源以促进放射学AI发展。
Recently, accurate mandible segmentation in CT scans based on deep learning methods has attracted much attention. However, there still exist two major challenges, namely, metal artifacts among mandibles and large variations in shape or size among individuals. To address these two challenges, we propose a recurrent segmentation convolutional neural network (RSegCNN) that embeds segmentation convolutional neural network (SegCNN) into the recurrent neural network (RNN) for robust and accurate segmentation of the mandible. Such a design of the system takes into account the similarity and continuit…
Background and Purpose: We aimed to develop and evaluate an automatic acute ischemic stroke-related (AIS) detection system involving a two-stage deep learning model. Methods: We included 238 cases from two different institutions. AIS-related findings were annotated on each of the 238 sets of head CT images by referring to head magnetic resonance imaging (MRI) images in which an MRI examination was performed within 24 h following the CT scan. These 238 annotated cases were divided into a training set including 189 cases and test set including 49 cases. Subsequently, a two-stage deep learning de…
Importance: Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. Objective: To develop and validate a set of deep learning algorithms for automated detection of following key findings from non-contrast head CT scans: intracranial hemorrhage (ICH) and its types, intraparenchymal (IPH), intraventricular (IVH), subdural (SDH), extradural (EDH) and subarachnoid (SAH) hemorrhages, calvarial fractures, midline shift and mass effect. Design and Settings: We retrospectively collected a dataset containing 313,318 head CT scans along with…
From birth to death, we all experience surprisingly ubiquitous changes over time due to aging. If we can predict aging in the digital domain, that is, the digital twin of the human body, we would be able to detect lesions in their very early stages, thereby enhancing the quality of life and extending the life span. We observed that none of the previously developed digital twins of the adult human body explicitly trained longitudinal conversion rules between volumetric medical images with deep generative models, potentially resulting in poor prediction performance of, for example, ventricular v…
StrokeNeXt: A Siamese-encoder Approach for Brain Stroke Classification in Computed Tomography Imagery
StrokeNeXt通过双分支编码器和轻量级解码器实现CT影像中脑卒中分类，达到高达0.988的准确率和F1分数，同时具备低推理时间和高鲁棒性。
Intracranial aneurysms are common nowadays and how to detect them intelligently is of great significance in digital health. While most existing deep learning research focused on medical images in a supervised way, we introduce an unsupervised method for the detection of intracranial aneurysms based on 3D point cloud data. In particular, our method consists of two stages: unsupervised pre-training and downstream tasks. As for the former, the main idea is to pair each point cloud with its jittered counterpart and maximise their correspondence. Then we design a dual-branch contrastive network wit…
Super-resolving medical images can help physicians in providing more accurate diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging (MRI) techniques capture several scans (modes) during a single investigation, which can jointly be used (in a multimodal fashion) to further boost the quality of super-resolution results. To this end, we propose a novel multimodal multi-head convolutional attention module to super-resolve CT and MRI scans. Our attention module uses the convolution operation to perform joint spatial-channel attention on multiple concatenated input …
Data augmentation can effectively resolve a scarcity of images when training machine-learning algorithms. It can make them more robust to unseen images. We present a lesion conditional Generative Adversarial Network LcGAN to generate synthetic Computed Tomography (CT) images for data augmentation. A lesion conditional image (segmented mask) is an input to both the generator and the discriminator of the LcGAN during training. The trained model generates contextual CT images based on input masks. We quantify the quality of the images by using a fully convolutional network (FCN) score and blurrin…
We propose Artificial Intelligence Prospective Randomized Observer Blinding Evaluation (AI-PROBE) for quantitative clinical performance evaluation of radiology AI systems within prospective randomized clinical trials. AI-PROBE encompasses a study design and a matching radiology IT infrastructure that randomly blinds radiologists for results provided by AI-based image analysis. To demonstrate the applicability of our evaluation framework, we present a first prospective randomized clinical trial on the effect of Intra-Cranial Hemorrhage (ICH) detection in emergent care head CT on radiology study…
Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN
提出融合多头通道注意力与加权全局平均池化的改进 Xception，用于自动分类 COVID-19 CT 图像，并在常用数据集上达到 96.99% 准确率。
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
提出通用且可扩展的Universal Model框架，利用语言驱动参数生成器和轻量级特定类别头，在多数据集上实现高效器官分割与肿瘤检测，展现强泛化能力并支持新类别扩展。
The recent advancements in deep learning have allowed for numerous applications in computed tomography (CT), with potential to improve diagnostic accuracy, speed of interpretation, and clinical efficiency. However, the deep learning community has to date neglected window display settings - a key feature of clinical CT interpretation and opportunity for additional optimization. Here we propose a window setting optimization (WSO) module that is fully trainable with convolutional neural networks (CNNs) to find optimal window settings for clinical performance. Our approach was inspired by the meth…
Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping is applied …
Semi-supervised learning for generalizable intracranial hemorrhage detection and segmentation
本文提出教师—学生半监督框架，利用25000例无标注CT的伪标签提升颅内出血检测与分割，并在CQ500分布外数据上验证其AUC、DSC和Pixel AP均显著优于监督基线。
A gradient-based approach to fast and accurate head motion compensation in cone-beam CT
本文提出一种基于梯度的CBCT运动估计方法，利用反投影算子的广义导数和体素级质量图回归网络，实现19倍加速并将重投影误差从3mm降至0.61mm，显著提升头部运动补偿的效率和准确性。
Privacy concerns around sharing personally identifiable information are a major practical barrier to data sharing in medical research. However, in many cases, researchers have no interest in a particular individual's information but rather aim to derive insights at the level of cohorts. Here, we utilize Generative Adversarial Networks (GANs) to create derived medical imaging datasets consisting entirely of synthetic patient data. The synthetic images ideally have, in aggregate, similar statistical properties to those of a source dataset but do not contain sensitive personal information. We ass…
Purpose: Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. Currently, IV contrast is poorly documented in imaging metadata and necessitates manual correction and annotation by clinician experts, presenting a major barrier to imaging analyses and algorithm deployment. We sought to develop and validate a convolutional neural network (CNN)-based deep learning (DL) platform to identify IV contrast within CT scans. Methods: For model development and evaluation, we used independent datasets of CT scans of head, neck (HN) …
UltraEar: a multicentric, large-scale database combining ultra-high-resolution computed tomography and clinical data for ear diseases
本文介绍多中心UltraEar数据库的构建，整合11家医院0.1 mm U-HRCT影像、报告与临床数据，并提供标准化标注、分割和隐私保护流程，以支持耳科AI研究、教学与协作。
Incorporating data-specific domain knowledge in deep networks explicitly can provide important cues beneficial for lesion detection and can mitigate the need for diverse heterogeneous datasets for learning robust detectors. In this paper, we exploit the domain information present in computed tomography (CT) scans and propose a robust universal lesion detection (ULD) network that can detect lesions across all organs of the body by training on a single dataset, DeepLesion. We analyze CT-slices of varying intensities, generated using heuristically determined Hounsfield Unit(HU) windows that indiv…
Towards Head Computed Tomography Image Reconstruction Standardization with Deep Learning Assisted Automatic Detection
针对头部CT图像3D重建中人工操作的主观性与偏差问题，提出基于深度学习目标检测的自动重建方法，通过比较十种算法选出YOLOv8实现92.77%的mAP，验证了临床有效性。
Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within 10.4ms. Mean average gradient magnitude (AGM) for the proposed algorithm was 57.2% higher than current state-of-the-art, while mean peak signal to noise ratio (PSNR), contrast to noise ratio (CNR), and structural similarity index metric (SSIM) increased by 11.1%, 154% and 187% respectively c…
Dynamic-Computed Tomography Angiography for Cerebral Vessel Templates and Segmentation
该研究面向脑血管CTA分割，构建群体平均血管图谱并训练深度学习模型，证明DL在动静脉分支分割上显著优于图谱法，并首次提出血管造影CT模板。
Detection and Localization of Subdural Hematoma Using Deep Learning on Computed Tomography
本文提出一种多模态深度学习框架，结合临床数据与CT影像，实现硬脑膜下出血的快速准确检测与定位，提升神经外科急诊处理效率。
Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT
本文提出viola-Unet注意力架构，通过融合空间正交与跨通道特征提升头部非增强CT的颅内出血分割性能，并在INSTANCE 2022挑战赛四项指标上均获第一。
Prediction of recurrence free survival of head and neck cancer using PET/CT radiomics and clinical information
本研究基于HECKTOR数据融合临床信息与PET/CT多模态影像组学特征构建CoxPH模型预测口咽头颈癌RFS，C-index达0.74，并揭示分割误差对两种模态影响不同。
Head Motion Degrades Machine Learning Classification of Alzheimer's Disease from Positron Emission Tomography
本研究揭示头部运动会降低基于PET的阿尔茨海默病机器学习分类准确率，通过两种AD相关示踪剂的队列验证了该偏差，强调了运动校正对发挥PET机器学习诊断能力的重要性。
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