Generate Structured Radiology Report from CT Images Using Image Annotation Techniques: Preliminary Results with Liver CT

Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review

Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e., a relationship between algorithm and users. Thus, prototyping and user evaluations are critical to attaining solutions that afford transparency. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users and the knowledge imbalance between those users and ML designers. To investigate the state of transparent ML in medical image analysis, we conducted a systematic review of the literature from 2012 to 2021 in PubMed, EMBASE, and Compendex databases. We identified 2508 records and 68 articles met the inclusion criteria. Current techniques in transparent ML are dominated by computational feasibility and barely consider end users, e.g. clinical stakeholders. Despite the different roles and knowledge of ML developers and end users, no study reported formative user research to inform the design and development of transparent ML models. Only a few studies validated transparency claims through empirical user evaluations. These shortcomings put contemporary research on transparent ML at risk of being incomprehensible to users, and thus, clinically irrelevant. To alleviate these shortcomings in forthcoming research, we introduce the INTRPRT guideline, a design directive for transparent ML systems in medical image analysis. The INTRPRT guideline suggests human-centered design principles, recommending formative user research as the first step to understand user needs and domain requirements. Following these guidelines increases the likelihood that the algorithms afford transparency and enable stakeholders to capitalize on the benefits of transparent ML.

Intelligent Labeling of Tumor Lesions Based on Positron Emission Tomography/Computed Tomography

Positron emission tomography/computed tomography (PET/CT) plays a vital role in diagnosing tumors. However, PET/CT imaging relies primarily on manual interpretation and labeling by medical professionals. An enormous workload will affect the training samples' construction for deep learning. The labeling of tumor lesions in PET/CT images involves the intersection of computer graphics and medicine, such as registration, a fusion of medical images, and labeling of lesions. This paper extends the linear interpolation, enhances it in a specific area of the PET image, and uses the outer frame scaling of the PET/CT image and the least-squares residual affine method. The PET and CT images are subjected to wavelet transformation and then synthesized in proportion to form a PET/CT fusion image. According to the absorption of 18F-FDG (fluoro deoxy glucose) SUV in the PET image, the professionals randomly select a point in the focus area in the fusion image, and the system will automatically select the seed point of the focus area to delineate the tumor focus with the regional growth method. Finally, the focus delineated on the PET and CT fusion images is automatically mapped to CT images in the form of polygons, and rectangular segmentation and labeling are formed. This study took the actual PET/CT of patients with lymphatic cancer as an example. The semiautomatic labeling of the system and the manual labeling of imaging specialists were compared and verified. The recognition rate was 93.35%, and the misjudgment rate was 6.52%.

Automatic Generation of Structured Radiology Reports for Volumetric Computed Tomography Images Using Question-Specific Deep Feature Extraction and Learning

BACKGROUND

In today's modern medicine, the use of radiological imaging devices has spread at medical centers. Therefore, the need for accurate, reliable, and portable medical image analysis and understanding systems has been increasing constantly. Accompanying images with the required clinical information, in the form of structured reports, is very important, because images play a pivotal role in detect, planning, and diagnosis of different diseases. Report-writing can be exposure to error, tedious and labor-intensive for physicians and radiologists; to address these issues, there is a need for systems that generate medical image reports automatically and efficiently. Thus, automatic report generation systems are among the most desired applications.

METHODS

This research proposes an automatic structured-radiology report generation system that is based on deep learning methods. Extracting useful and descriptive image features to model the conceptual contents of the images is one of the main challenges in this regard. Considering the ability of deep neural networks (DNNs) in soliciting informative and effective features as well as lower resource requirements, tailored convolutional neural networks and MobileNets are employed as the main building blocks of the proposed system. To cope with challenges such as multi-slice medical images and diversity of questions asked in a radiology report, our system develops volume-level and question-specific deep features using DNNs.

RESULTS

We demonstrate the effectiveness of the proposed system on ImageCLEF2015 Liver computed tomography (CT) annotation task, for filling in a structured radiology report about liver CT. The results confirm the efficiency of the proposed approach, as compared to classic annotation methods.

CONCLUSION

We have proposed a question-specific DNNbased system for filling in structured radiology reports about medical images.

COVID-19–affected medical image analysis using DenserNet

The COrona VIrus Disease (COVID-19) outbreak has been announced as a pandemic by the World Health Organization (WHO) in mid-February 2020. With the current pandemic situation, the testing and detection of this disease are becoming a challenge in many regions across the globe because of the insufficiency of the suitable testing infrastructure. The shortage of kits to test COVID-19 has led to another crisis owing to worldwide supply-demand mismatch, and thereby, widen up a new research area that deals with the detection of COVID-19 without the test kit. In this paper, we investigate medical images, mostly chest X-ray images and thorax computed tomography (CT) scans to identify the attack of COVID-19. In countries, where the number of medical experts is lesser than the expected as recommended by WHO, this computer-aided system can be useful as it requires minimal human intervention. Consequently, this technology reduces the chances of contagious infection. This study may further help in the early detection of people with some similar symptoms of coronavirus. Early detection and intervention can play a pivotal role in coronavirus treatment. The primary goal of our work is to detect COVID-19–affected cases. However, this work can be extended to detect pneumonia because of Severe Acute Respiratory Syndrome, Acute Respiratory Distress Syndrome, Middle East Respiratory Syndrome, and bacteria-like Streptococcus. In this paper, we employ publicly available medical images obtained from various demographics, and propose a rapid cost-effective test leveraging a deep learning-based framework. Here, we propose a new architecture based on a densely connected convolutional neural network to analyze the COVID-19–affected medical images. We name our proposed architecture as DenserNet, which is an improvisation of DenseNet. Our proposed Denser Net architecture achieved 96.18% and 87.19% accuracies on two publicly available databases containing chest X-ray images and thorax CT scans, respectively, for the task of separating COVID-19 and non-COVID-19 images, which is quite encouraging.