Institute of Fundamental Technological Research
Polish Academy of Sciences

Staff

Piotr Jarosik, MSc

Department of Information and Computational Science (ZIiNO)
Division of Computational Materials Science (PMKIM)
position: doctoral student
telephone: (+48) 22 826 12 81 ext.: 412
room: 414
e-mail:


Recent publications
1.  Byra M., Jarosik P., Szubert A., Galperine M., Ojeda-Fournier H., Olson L., O'Boyle M., Comstock Ch., Andre M., Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network, Biomedical Signal Processing and Control, ISSN: 1746-8094, DOI: 10.1016/j.bspc.2020.102027, Vol.61, pp.102027-1-10, 2020

Abstract:
In this work, we propose a deep learning method for breast mass segmentation in ultrasound (US). Variations in breast mass size and image characteristics make the automatic segmentation difficult. To addressthis issue, we developed a selective kernel (SK) U-Net convolutional neural network. The aim of the SKswas to adjust network's receptive fields via an attention mechanism, and fuse feature maps extractedwith dilated and conventional convolutions. The proposed method was developed and evaluated usingUS images collected from 882 breast masses. Moreover, we used three datasets of US images collectedat different medical centers for testing (893 US images). On our test set of 150 US images, the SK-U-Netachieved mean Dice score of 0.826, and outperformed regular U-Net, Dice score of 0.778. When evaluatedon three separate datasets, the proposed method yielded mean Dice scores ranging from 0.646 to 0.780. Additional fine-tuning of our better-performing model with data collected at different centers improvedmean Dice scores by ~6%. SK-U-Net utilized both dilated and regular convolutions to process US images. We found strong correlation, Spearman's rank coefficient of 0.7, between the utilization of dilated convo-lutions and breast mass size in the case of network's expansion path. Our study shows the usefulness ofdeep learning methods for breast mass segmentation. SK-U-Net implementation and pre-trained weightscan be found at github.com/mbyr/bus_seg.

Keywords:
attention mechanism, breast mass segmentation, convolutional neural networks, deep learning, receptive field, ultrasound imaging

Affiliations:
Byra M. - IPPT PAN
Jarosik P. - IPPT PAN
Szubert A. - other affiliation
Galperine M. - other affiliation
Ojeda-Fournier H. - University of California (US)
Olson L. - University of California (US)
Comstock Ch. - Memorial Sloan-Kettering Cancer Center (US)
Andre M. - University of California (US)
2.  Jarosik P., Klimonda Z., Lewandowski M., Byra M., Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks, Biocybernetics and Biomedical Engineering, ISSN: 0208-5216, DOI: 10.1016/j.bbe.2020.04.002, Vol.40, No.3, pp.977-986, 2020

Abstract:
We propose a novel approach to breast mass classification based on deep learning models that utilize raw radio-frequency (RF) ultrasound (US) signals. US images, typically displayed by US scanners and used to develop computer-aided diagnosis systems, are reconstructed using raw RF data. However, information related to physical properties of tissues present in RF signals is partially lost due to the irreversible compression necessary to make raw data readable to the human eye. To utilize the information present in raw US data, we develop deep learning models that can automatically process small 2D patches of RF signals and their amplitude samples. We compare our approach with classification method based on the Nakagami parameter, a widely used quantitative US technique utilizing RF data amplitude samples. Our better performing deep learning model, trained using RF signals and their envelope samples, achieved good classification performance, with the area under the receiver attaining operating characteristic curve (AUC) and balanced accuracy of 0.772 and 0.710, respectively. The proposed method significantly outperformed the Nakagami parameter-based classifier, which achieved AUC and accuracy of 0.64 and 0.611, respectively. The developed deep learning models were used to generate parametric maps illustrating the level of mass malignancy. Our study presents the feasibility of using RF data for the development of deep learning breast mass classification models.

Keywords:
breast lesion classification, convolutional neural networks, deep learning, radio-frequency signals, ultrasound imaging

Affiliations:
Jarosik P. - IPPT PAN
Klimonda Z. - IPPT PAN
Lewandowski M. - IPPT PAN
Byra M. - IPPT PAN

List of chapters in recent monographs
1. 
Kidziński Ł., Mohanty S.P., Ong C.F., Huang Z., Zhou S., Pechenko A., Stelmaszczyk A., Jarosik P., Pavlov M., Kolesnikov S., Plis S., Chen Z., Zhang Z., Chen J., Shi J., Zheng Z., Yuan Ch., Lin Z., Michalewski H., Milos P., Osinski B., Melnik A., Schilling M., Ritter H., Carroll S.F., Hicks J., Levine S., Salathé M., Delp S., The NIPS '17 Competition: Building Intelligent Systems, rozdział: Learning to Run Challenge Solutions: Adapting Reinforcement Learning Methods for Neuromusculoskeletal Environments, Springer, pp.121-153, 2018

Conference papers
1.  Lewandowski M., Jarosik P., Tasinkevych Y., Walczak M., Efficient GPU implementation of 3D spectral domain synthetic aperture imaging, IUS 2020, IEEE International Ultrasonics Symposium, 2020-09-07/09-11, Las Vegas (US), DOI: 10.1109/IUS46767.2020.9251552, pp.1-3, 2020

Abstract:
In this work, we considered the implementation of a 3D volume reconstruction algorithm for single plane-wave ultrasound insonification. We review the theory behind the Hybrid Spectral-Domain Imaging (HSDI) algorithm, provide details of the algorithm implementation for Nvidia CUDA GPU cards, and discuss the performance evaluation results. The average time required to reconstruct a single data volume using our GPU implementation of the HSDI algorithm was 22 ms. We also present an iso-surface extraction result using a marching cubes algorithm. Our work constitutes a preliminary research for further development and implementation of 3D volume reconstruction using GPU implementation of the spectral domain imaging algorithm.

Keywords:
ultrasound imaging, 3D ultrasound, volumetric imaging, gpu

Affiliations:
Lewandowski M. - IPPT PAN
Jarosik P. - IPPT PAN
Tasinkevych Y. - IPPT PAN
Walczak M. - IPPT PAN
2.  Jarosik P., Lewandowski M., Automatic Ultrasound Guidance Based on Deep Reinforcement Learning, IUS 2019, IEEE, International Ultrasonics Symposium, 2019-10-06/10-09, Glasgow (GB), DOI: 10.1109/ULTSYM.2019.8926041, pp.475-478, 2019

Abstract:
Ultrasound is becoming the modality of choice for everyday medical diagnosis, due to its mobility and decreasing price. As the availability of ultrasound diagnostic devices for untrained users grows, appropriate guidance becomes desirable. This kind of support could be provided by a software agent, who easily adapts to new conditions, and whose role is to instruct the user on how to obtain optimal settings of the imaging system during an examination. In this work, we verified the feasibility of implementing and training such an agent for ultrasound, taking the deep reinforcement learning approach. The tasks it was given were to find the optimal position of the transducer’s focal point (FP task) and to find an appropriate scanning plane (PP task). The ultrasound environment consisted of a linear-array transducer acquiring information from a tissue phantom with cysts forming an object-of-interest (OOI). The environment was simulated in the Field-II software. The agent could perform the following actions: move the position of the probe to the left/right, move focal depth upwards/downwards, rotate the probe clockwise/counter-clockwise, or do not move. Additional noise was applied to the current probe setting. The only observations the agent received were B-mode frames. The agent acted according to stochastic policy modeled by a deep convolutional neural network, and was trained using the vanilla policy gradient update algorithm. After the training, the agent’s ability to accurately locate the position of the focal depth and scanning plane improved. Our preliminary results confirmed that deep reinforcement learning can be applied to the ultrasound environment.

Keywords:
ultrasound guidance, reinforcement learning, deep learning

Affiliations:
Jarosik P. - IPPT PAN
Lewandowski M. - IPPT PAN
3.  Jarosik P., Lewandowski M., The feasibility of deep learning algorithms integration on a GPU-based ultrasound research scanner, IUS 2017, IEEE International Ultrasonics Symposium, 2017-09-06/09-09, Washington (US), DOI: 10.1109/ULTSYM.2017.8091750, pp.1-4, 2017

Abstract:
Ultrasound medical diagnostics is a real-time modality based on a doctor's interpretation of images. So far, automated Computer-Aided Diagnostic tools were not widely applied to ultrasound imaging. The emerging methods in Artificial Intelligence, namely deep learning, gave rise to new applications in medical imaging modalities. The work's objective was to show the feasibility of implementing deep learning algorithms directly on a research scanner with GPU software beamforming. We have implemented and evaluated two deep neural network architectures as part of the signal processing pipeline on the ultrasound research platform USPlatform (us4us Ltd., Poland). The USPlatform is equipped with a GPU cluster, enabling full software-based channel data processing as well as the integration of open source Deep Learning frameworks. The first neural model (S-4-2) is a classical convolutional network for one-class classification of baby body parts. We propose a simple 6-layer network for this task. The model was trained and evaluated on a dataset consisting of 786 ultrasound images of a fetal training phantom. The second model (Gu-net) is a fully convolutional neural network for brachial plexus localisation. The model uses 'U-net'-like architecture to compute the overall probability of target detection and the probability mask of possible target locations. The model was trained and evaluated on 5640 ultrasound B-mode frames. Both training and inference were performed on a multi-GPU (Nvidia Titan X) cluster integrated with the platform. As performance metrics we used: accuracy as a percentage of correct answers in classification, dice coefficient for object detection, and mean and std. dev. of a model's response time. The 'S-4-2' model achieved 96% classification accuracy and a response time of 3 ms (334 predictions/s). This simple model makes accurate predictions in a short time. The 'Gu-net' model achieved a 0.64 dice coefficient for object detection and a 76% target's presence classification accuracy with a response time of 15 ms (65 predictions/s). The brachial plexus detection task is more challenging and requires more effort to find the right solution. The results show that deep learning methods can be successfully applied to ultrasound image analysis and integrated on a single advanced research platform

Keywords:
Ultrasonic imaging, Neural networks, Convolution, Machine learning, Image segmentation, Kernel

Affiliations:
Jarosik P. - IPPT PAN
Lewandowski M. - IPPT PAN

Conference abstracts
1.  Jarosik P., Byra M., Lewandowski M., Waveflow - Towards Integration of Ultrasound Processing with Deep Learning, IUS 2018, IEEE International Ultrasonics Symposium, 2018-10-22/10-25, KOBE (JP), pp.1-3, 2018

Abstract:
The ultimate goal of this work is a real-time processing framework for ultrasound image reconstruction augmented with machine learning. To attain this, we have implemented WaveFlow – a set of ultrasound data acquisition and processing tools for TensorFlow. WaveFlow includes: ultrasound Environments (connection points between the input raw ultrasound data source and TensorFlow) and signal processing Operators (ops) library. Raw data can be processed in real-time using algorithms available both in TensorFlow and WaveFlow. Currently, WaveFlow provides ops for B-mode image econstruction (beamforming), signal processing and quantitative ultrasound. The ops were implemented both for the CPU and GPU, as well as for built-in automated tests and benchmarks. To demonstrate WaveFlow’s performance, ultrasound data were acquired from wire and cyst phantoms and elaborated using selected sequences of the ops. We implemented and valuated: Delay-and-Sum beamformer, synthetic transmit aperture imaging (STAI), planewave imaging (PWI), envelope detection algorithm and dynamic range clipping. The benchmarks were executed on the NVidiaR Titan X GPU integrated in the USPlatform research scanner (us4us Ltd., Poland). We achieved B-mode image reconstruction frame rates of 55 fps, 17 fps for the STAI and the PWI algorithms, respectively. The results showed the feasibility of realtime ultrasound image reconstruction using WaveFlow operatorsin the TensorFlow framework. WaveFlow source code can be found at github.com/waveflow-team/waveflow.

Affiliations:
Jarosik P. - IPPT PAN
Byra M. - IPPT PAN
Lewandowski M. - IPPT PAN

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