Risk Assessment of Computer-Aided Diagnostic Software for Hepatic Resection

Yang

Balakrishnan

et al. 2022

Sci Rep

Self Cite

Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.

Section: Introductionmentioning

confidence: 99%

A lightweight neural network with multiscale feature enhancement for liver CT segmentation

Yang

Balakrishnan

et al. 2022

Sci Rep

Self Cite

“…The collaborative model obtains promising results and an average segmentation accuracy of 80%. On the other hand, fully automated techniques lack performance because of the complex and volatile nature of surgeries and complications [86]. Nonetheless, complete automation is being consistently pursued to achieve performance that is comparable to semi-automatic methods.…”

Section: Automationmentioning

confidence: 99%

Practical Utility of Liver Segmentation Techniques in Clinical Surgeries and Interventions

Dakua

Abdalla

et al. 2022

Preprint

Self Cite

Medical images (e.g., magnetic resonance imaging (MRI) and computed tomography (CT)) provide critical information to the clinicians in order to diagnose pathology and plan interventions. Image segmentation is the first and foremost step taken by the clinicians while optimizing analytic diagnosis and treatment planning for interventions (e.g., transplantation and complete resection) and therapeutic procedures (e.g., radiotherapy, PVE, and embolization approaches), especially in hepatocellular carcinoma. Thus, segmentation techniques certainly impact the diagnosis and treatment outcomes. This paper studies the literature during the year 2012 until 2021 and reviews the segmentation methods classifying them into three categories based on their clinical utility (i.e., surgical and radiological interventions). The classification is based on the parameters such as precision, accuracy, location, liver condition, and other clinical considerations.

“…Due to the aforementioned applications, food computing has been highlighted as an important research direction from the research community due to its benefits and wide use cases. In recent years, automatic food recognition (AFR) has received renewed attention due to the success of deep learning models in classification tasks of computer vision and multimedia applications [1]- [3]. The benefits and application of AFR is wide and diverse.…”

Section: Introductionmentioning

confidence: 99%

“…1 framework. All images have been resized to 224×224×3 for all models except for ResNet-50, which requires input images of dimensions 229×229×3.…”

mentioning

confidence: 99%

MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East

Qaraqe

2023

IEEE Access

Automatic food recognition systems have been receiving increasing attention in the research community with the advancements in inductive learning (e.g., classification in computer vision) due to their applicability in the healthcare and hospitality industry. However, food recognition is challenging due to its fine-grained nature and its high correlation with culture, geo-location, and language. To make food recognition systems feasible for the Middle Eastern region, we present a large-scale dataset (MEFood) of commonly consumed food items in the Middle East, thereby providing a dataset for current development and establishing a benchmark for future research. We have also thoroughly examined the MEFood dataset highlighting its challenging aspects and its real-world nature. Additionally, we have conducted a thorough experimental study benchmarking the mainstream computer vision and mobile networks on classification, runtime, and resource utilization metrics. Our results highlight that EfficientNet-V2 achieves performance closer to the best-performing individual model on the MEFood dataset while having the least resource utilization and minimal inference times. Finally, we have performed a thorough error analysis study to glean additional insights about the networks and MEFood dataset.