Automatic detection of over 100 anatomical landmarks in medical CT images: A framework with independent detectors and combinatorial optimization

A spatio-temporal graph convolutional network for ultrasound echocardiographic landmark detection

Landmark detection is a crucial task in medical image analysis, with applications across various fields. However, current methods struggle to accurately locate landmarks in medical images with blurred tissue boundaries due to low image quality. In particular, in echocardiography, sparse annotations make it challenging to predict landmarks with position stability and temporal consistency. In this paper, we propose a spatio-temporal graph convolutional network tailored for echocardiography landmark detection. We specifically sample landmark labels from the left ventricular endocardium and pre-calculate their correlations to establish structural priors. Our approach involves a graph convolutional neural network that learns the interrelationships among landmarks, significantly enhancing landmark accuracy within ambiguous tissue contexts. Additionally, we integrate gate recurrent units to grasp the temporal consistency of landmarks across consecutive images, augmenting the model's resilience against unlabeled data. Through validation across three echocardiography datasets, our method demonstrates superior accuracy when contrasted with alternative landmark detection models.

Multi-target landmark detection with incomplete images via reinforcement learning and shape prior embedding

Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets. Based on learning a navigation policy, instead of predicting targets directly, reinforcement learning (RL)-based methods have the potential to tackle this challenge in an efficient manner. Inspired by this, in this work we propose a multi-agent RL framework for simultaneous multi-target landmark detection. This framework is aimed to learn from incomplete or (and) complete images to form an implicit knowledge of global structure, which is consolidated during the training stage for the detection of targets from either complete or incomplete test images. To further explicitly exploit the global structural information from incomplete images, we propose to embed a shape model into the RL process. With this prior knowledge, the proposed RL model can not only localize dozens of targets simultaneously, but also work effectively and robustly in the presence of incomplete images. We validated the applicability and efficacy of the proposed method on various multi-target detection tasks with incomplete images from practical clinics, using body dual-energy X-ray absorptiometry (DXA), cardiac MRI and head CT datasets. Results showed that our method could predict whole set of landmarks with incomplete training images up to 80% missing proportion (average distance error 2.29 cm on body DXA), and could detect unseen landmarks in regions with missing image information outside FOV of target images (average distance error 6.84 mm on 3D half-head CT). Our code will be released via https://zmiclab.github.io/projects.html.

Hybrid curvature-geometrical detection of landmarks for the automatic analysis of the reduction of supracondylar fractures of the femur

BACKGROUND AND OBJECTIVE

The analysis of the features of certain tissues is required by many procedures of modern medicine, allowing the development of more efficient treatments. The recognition of landmarks allows the planning of orthopedic and trauma surgical procedures, such as the design of prostheses or the treatment of fractures. Formerly, their detection has been carried out by hand, making the workflow inaccurate and tedious. In this paper we propose an automatic algorithm for the detection of landmarks of human femurs and an analysis of the quality of the reduction of supracondylar fractures.

METHODS

The detection of anatomical landmarks follows a knowledge-based approach, consisting of a hybrid strategy: curvature and spatial decomposition. Prior training is unrequired. The analysis of the reduction quality is performed by a side-to-side comparison between healthy and fractured sides. The pre-clinical validation of the technique consists of a two-stage study: Initially, we tested our algorithm with 14 healthy femurs, comparing the output with ground truth values. Then, a total of 140 virtual fractures was processed to assess the validity of our analysis of the quality of reduction. A two-sample t test and correlation coefficients between metrics and the degree of reduction have been employed to determine the reliability of the algorithm.

RESULTS

The average detection error of landmarks was maintained below 1.7 mm and 2^∘ (p< 0.01) for points and axes, respectively. Regarding the contralateral analysis, the resulting P-values reveal the possibility to determine whether a supracondylar fracture is properly reduced or not with a 95% of confidence. Furthermore, the correlation is high between the metrics and the quality of the reduction.

CONCLUSIONS

This research concludes that our technique allows to classify supracondylar fracture reductions of the femur by only analyzing the detected anatomical landmarks. A initial training set is not required as input of our algorithm.

Effectiveness of temporal subtraction computed tomography images using deep learning in detecting vertebral bone metastases

PURPOSE

To assess the clinical effectiveness of temporal subtraction computed tomography (TS CT) using deep learning to improve vertebral bone metastasis detection.

METHOD

This retrospective study used TS CT comprising bony landmark detection, bone segmentation with a multi-atlas-based method, and spatial registration of two images by a log-domain diffeomorphic Demons algorithm. Paired current and past CT images of 50 patients without vertebral metastasis, recorded during June 2011-September 2016, were included as training data. A deep learning-based method estimated registration errors and suppressed false positives. Thereafter, paired CT images of 40 cancer patients with newly developed vertebral metastases and 40 control patients without vertebral metastases were evaluated. Six board-certified radiologists and five radiology residents independently interpreted 80 paired CT images with and without TS CT.

RESULTS

Records of 40 patients in the metastasis group (median age: 64.5 years; 20 males) and 40 patients in the control group (median age: 64.0 years; 20 males) were evaluated. With TS CT, the overall figure of merit (FOM) of the board-certified radiologist and resident groups improved from 0.848 to 0.876 (p = 0.01) and from 0.752 to 0.799 (p = 0.02), respectively. The sub-analysis focusing on attenuation changes in lesions revealed that the FOM of osteoblastic lesions significantly improved in both the board-certified radiologist and resident groups using TS CT. The sub-analysis focusing on lesion location showed that the FOM of the resident group significantly improved in the vertebral arch (p = 0.04).

CONCLUSIONS

TS CT was effective in detecting bone metastasis by both board-certified radiologists and radiology residents.

Automatic detection of landmarks for the analysis of a reduction of supracondylar fractures of the humerus

An accurate identification of bone features is required by modern orthopedics to improve patient recovery. The analysis of landmarks enables the planning of a fracture reduction surgery, designing prostheses or fixation devices, and showing deformities accurately. The recognition of these features was previously performed manually. However, this long and tedious process provided insufficient accuracy. In this paper, we propose a geometrically-based algorithm that automatically detects the most significant landmarks of a humerus. By employing contralateral images of the upper limb, a side-to-side study of the landmarks is also conducted to analyze the goodness of supracondylar fracture reductions. We conclude that a reduction can be classified by only considering the detected landmarks. In addition, our technique does not require a prior training, thus becoming a reliable alternative to treat this kind of fractures.

Quality of Service aware Medical CT Image Transmission Anti-collision Mechanism Based on Big Data Autonomous Anti-collision Control

Background:

At present, due to the limitation of hardware, software and network transmission performance, the medical diagnosis of medical CT image equipment is easy to be carried out based on the wrong image. In addition, due to the complex structure of human organs and unpredictable lesion location, it is difficult to judge the reliability of medical CT images, spatial localization of the lesion, two-dimensional slice images and shape based on stereotypes. Therefore, how to improve the efficiency of medical CT terminal and the image quality has become the key technology to improve the satisfaction of medical diagnosis and treatment.

Objective:

To improve the work efficiency of medical CT terminal and medical image transmission quality, with the medical CT terminal state and service quality.

Methods:

Firstly, from the view of throughput, packet loss rate, delay and so on, a QoS aware model for medical CT image transmission has been established. Then, with throughput, packet length, path loss, service area size, access point location, and the number of medical CT terminals, the performance change regulation of the medical CT image transmission is completed and the optimal quality of service guarantee parameters sequence is obtained. Next, the medical CT image big data autonomous collision control scheme is proposed.

Results:

The experimental and mathematical results verify the real-time performance, reliability, effectiveness and feasibility of the proposed medical CT image transmission anti-collision mechanism.

Conclusion:

The proposed scheme can satisfy the high-quality high demand for data transmission at the same time, according to a variety of user experience demand and real-time adjustment of medical CT terminal working state, which provides effective data quality assurance and optimization of the network source distribution, and also enhances the quality of medical image data transmission service.

Landmark Typology in Applied Morphometrics Studies: What's the Point?

Landmarks are the hallmark of biological shape analysis as discrete anatomical points of correspondence. Various systems have been developed for their classification. In the most widely used system, developed by Bookstein in the 1990s, landmarks are divided into three distinct types based on their anatomical locations and biological significance. As Bookstein and others have argued that different landmark types possess different qualities, e.g., that Type 3 landmarks contain deficient information about shape variation and are less reliably measured, researchers began using landmark types as justification for selecting or avoiding particular landmarks for measurement or analysis. Here, we demonstrate considerable variation in landmark classifications among 17 studies using geometric morphometrics (GM), due to disagreement in the application of both Bookstein's landmark typology and individual landmark definitions. A review of the literature furthermore shows little correlation between landmark type and measurement reproducibility, especially when factors such as differences in measurement tools (calipers, digitizer, or computer software) and data sources (dry crania, 3D models, or 2D images) are considered. Although landmark typology is valuable when teaching biological shape analysis, we find that employing it in research design introduces confusion without providing useful information. Instead, researchers should choose landmark configurations based on their ability to test specific research hypotheses, and research papers should include justifications of landmark choices along with landmark definitions, details on landmark collection methods, and appropriate interobserver and intraobserver analyses. Hence, while the landmarks themselves are crucial for GM, we argue that their typology is of little use in applied studies. Anat Rec, 302:1144-1153, 2019. © 2018 Wiley Periodicals, Inc.

Towards intelligent robust detection of anatomical structures in incomplete volumetric data

Robust and fast detection of anatomical structures represents an important component of medical image analysis technologies. Current solutions for anatomy detection are based on machine learning, and are generally driven by suboptimal and exhaustive search strategies. In particular, these techniques do not effectively address cases of incomplete data, i.e., scans acquired with a partial field-of-view. We address these challenges by following a new paradigm, which reformulates the detection task to teaching an intelligent artificial agent how to actively search for an anatomical structure. Using the principles of deep reinforcement learning with multi-scale image analysis, artificial agents are taught optimal navigation paths in the scale-space representation of an image, while accounting for structures that are missing from the field-of-view. The spatial coherence of the observed anatomical landmarks is ensured using elements from statistical shape modeling and robust estimation theory. Experiments show that our solution outperforms marginal space deep learning, a powerful deep learning method, at detecting different anatomical structures without any failure. The dataset contains 5043 3D-CT volumes from over 2000 patients, totaling over 2,500,000 image slices. In particular, our solution achieves 0% false-positive and 0% false-negative rates at detecting whether the landmarks are captured in the field-of-view of the scan (excluding all border cases), with an average detection accuracy of 2.78 mm. In terms of runtime, we reduce the detection-time of the marginal space deep learning method by 20-30 times to under 40 ms, an unmatched performance for high resolution incomplete 3D-CT data.

Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems-a New Paradigm

We propose a generalized framework for developing computer-aided detection (CADe) systems whose characteristics depend only on those of the training dataset. The purpose of this study is to show the feasibility of the framework. Two different CADe systems were experimentally developed by a prototype of the framework, but with different training datasets. The CADe systems include four components; preprocessing, candidate area extraction, candidate detection, and candidate classification. Four pretrained algorithms with dedicated optimization/setting methods corresponding to the respective components were prepared in advance. The pretrained algorithms were sequentially trained in the order of processing of the components. In this study, two different datasets, brain MRA with cerebral aneurysms and chest CT with lung nodules, were collected to develop two different types of CADe systems in the framework. The performances of the developed CADe systems were evaluated by threefold cross-validation. The CADe systems for detecting cerebral aneurysms in brain MRAs and for detecting lung nodules in chest CTs were successfully developed using the respective datasets. The framework was shown to be feasible by the successful development of the two different types of CADe systems. The feasibility of this framework shows promise for a new paradigm in the development of CADe systems: development of CADe systems without any lesion specific algorithm designing.