A deep learning based approach for classification of abdominal organs using ultrasound images

Reddy, D Santhosh; Rajalakshmi, P.; Mateen, M.A.

doi:10.1016/j.bbe.2021.05.004

Cited by 10 publications

(8 citation statements)

References 28 publications

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“…The experiment was performed on a computer with an Intel CPU with a clock speed of 2.4 GHz and a Nvidia 20 series GPU using the Pytorch framework [ 33 ] and Cuda toolkit (version 11.6). As with previous literature [ 26 , 28 , 29 ] publicly available neural networks pre-trained on the ImageNet challenge dataset [ 27 ] were used as the basis for transfer learning. The neural networks architectures chosen for this experiment can be classified by the principles behind their design.…”

Section: Methodsmentioning

confidence: 99%

“…Xu et al [ 28 ] examined classification of 11 ultrasound abdominal cross sections as part of a wider study on landmark detection, the Single-task learning (STL) ResNet-50 attained an accuracy of 81.22% in comparison to the radiologist who achieved 78.87%. Reddy et al [ 29 ], tested a number of neural networks on 6 visually distinct abdominal cross sections achieving an accuracy of 98.77% using a ResNet-50.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections

Lawley,

Hampson,

Worrall

et al. 2024

Abdom Radiol

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Purpose Abdominal ultrasound screening requires the capture of multiple standardized plane views as per clinical guidelines. Currently, the extent of adherence to such guidelines is dependent entirely on the skills of the sonographer. The use of neural network classification has the potential to better standardize captured plane views and streamline plane capture reducing the time burden on operators by combatting operator variability. Methods A dataset consisting of 16 routine upper abdominal ultrasound scans from 64 patients was used to test the classification accuracy of 9 neural networks. These networks were tested on both a small, idealised subset of 800 samples as well as full video sweeps of the region of interest using stratified sampling and transfer learning. Results The highest validation accuracy attained by both GoogLeNet and InceptionV3 is 83.9% using transfer learning and the large sample set of 26,294 images. A top-2 accuracy of 95.1% was achieved using InceptionV3. Alexnet attained the highest accuracy of 79.5% (top-2 of 91.5%) for the smaller sample set of 800 images. The neural networks evaluated during this study were also successfully able to identify problematic individual cross sections such as between kidneys, with right and left kidney being accurately identified 78.6% and 89.7%, respectively. Conclusion Dataset size proved a more important factor in determining accuracy than network selection with more complex neural networks providing higher accuracy as dataset size increases and simpler linear neural networks providing better results where the dataset is small.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections

Lawley,

Hampson,

Worrall

et al. 2024

Abdom Radiol

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show abstract

“…These studies show reduced accuracy where cross sections overlap or have visual similarities. Where a distinct dataset is used, that avoids these overlaps and visual similarities, accuracies of between 95.7% and 98.6% can be achieved [7]. This further highlights the limitations of using an image-only approach for abdominal cross sections, due to the lack of distinctive landmarks where there are overlapping classes within the imagery.…”

Section: Introductionmentioning

confidence: 95%

“…(e) Transverse approach of the left kidney (f) Transverse approach of the right kidney. These cross sections were chosen specifically based on classification error in previous studies [3][4][5]7] and due to visual similarity, such as with the left and right kidneys and over lapping region of interest (ROI) such as with Gall bladder and Common Bile Duct. Complex sweep scans of aorta and portal veins that contain both visual similarities and overlapping anatomical structures were also chosen to provide added complexity to classification.…”

Section: Datasetmentioning

confidence: 99%

Using positional tracking to improve abdominal ultrasound machine learning classification

Lawley,

Hampson,

Worrall

et al. 2024

Mach. Learn.: Sci. Technol.

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Diagnostic abdominal ultrasound screening and monitoring protocols are based around gathering a set of standard cross sectional images that ensure the coverage of relevant anatomical structures during the collection procedure. This allows clinicians to make diagnostic decisions with the best picture available from that modality. Currently, there is very little assistance provided to sonographers to ensure aderence to collection protocols, with previous studies suggesting that traditional image only machine learning classification can provide only limited assistance in supporting this task, for example it can be difficult to differentiate between multiple liver cross sections or those of the left and right kidney from image post collection. In this proof of concept, positional tracking information was added to the image input of a neural network to provide the additional context required to recognize six otherwise difficult to identify edge cases. In this paper optical and sensor based infrared tracking (IR) was used to track the position of an ultrasound probe during the collection of clinical cross sections on an abdominal phantom. Convolutional neural networks were then trained using both image-only and image with positional data, the classification accuracy results were then compared. The addition of positional information significantly improved average classification results from ~90% for image-only to 95% for optical IR position tracking and 93% for Sensor-based IR in common abdominal cross sections. While there is further work to be done, the addition of low-cost positional tracking to machine learning ultrasound classification will allow for significantly increased accuracy for identifying important diagnostic cross sections, with the potential to not only provide validation of adherence to protocol but also could provide navigation prompts to assist in user training and in ensuring aderence in capturing cross sections in future.

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“…The above studies all use the CNN network to study related diseases, but there were problems such as a lack of context information and fine‐grained features, the need for large number of parameters and computational costs, long computing times, and a loss of effective information due to small input data sizes. There were few studies on the classification of HCC and MHC using deep learning methods, and some studies used large deep networks to classify normal and abnormal livers 18,19 …”

Section: Introductionmentioning

confidence: 99%

Classification of metastatic hepatic carcinoma and hepatocellular carcinoma lesions using contrast‐enhanced CT based on EI‐CNNet

et al. 2023

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BackgroundFor hepatocellular carcinoma and metastatic hepatic carcinoma, imaging is one of the main diagnostic methods. In clinical practice, diagnosis mainly relied on experienced imaging physicians, which was inefficient and cannot met the demand for rapid and accurate diagnosis. Therefore, how to efficiently and accurately classify the two types of liver cancer based on imaging is an urgent problem to be solved at present.PurposeThe purpose of this study was to use the deep learning classification model to help radiologists classify the single metastatic hepatic carcinoma and hepatocellular carcinoma based on the enhanced features of enhanced CT (Computer Tomography) portal phase images of the liver site.MethodsIn this retrospective study, 52 patients with metastatic hepatic carcinoma and 50 patients with hepatocellular carcinoma were among the patients who underwent preoperative enhanced CT examinations from 2017–2020. A total of 565 CT slices from these patients were used to train and validate the classification network (EI‐CNNet, training/validation: 452/113). First, the EI block was used to extract edge information from CT slices to enrich fine‐grained information and classify them. Then, ROC (Receiver Operating Characteristic) curve was used to evaluate the performance, accuracy, and recall of the EI‐CNNet. Finally, the classification results of EI‐CNNet were compared with popular classification models.ResultsBy utilizing 80% data for model training and 20% data for model validation, the average accuracy of this experiment was 98.2% ± 0.62 (mean ± standard deviation (SD)), the recall rate was 97.23% ± 2.77, the precision rate was 98.02% ± 2.07, the network parameters were 11.83 MB, and the validation time was 9.83 s/sample. The classification accuracy was improved by 20.98% compared to the base CNN network and the validation time was 10.38 s/sample. Compared with other classification networks, the InceptionV3 network showed improved classification results, but the number of parameters was increased and the validation time was 33 s/sample, and the classification accuracy was improved by 6.51% using this method.ConclusionEI‐CNNet demonstrated promised diagnostic performance and has potential to reduce the workload of radiologists and may help distinguish whether the tumor is primary or metastatic in time; otherwise, it may be missed or misjudged.

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A deep learning based approach for classification of abdominal organs using ultrasound images

Cited by 10 publications

References 28 publications

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections

Using positional tracking to improve abdominal ultrasound machine learning classification

Classification of metastatic hepatic carcinoma and hepatocellular carcinoma lesions using contrast‐enhanced CT based on EI‐CNNet

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