Piotr Jarosik, M.Sc., Eng.

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: pjarosik

List of chapters in recent monographs
1.
602
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.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), 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
2.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