Synopsis

Cell based therapy (CBT) is promising for treating a number of diseases. The ability to serially and non-invasively measure the number and determine the precise location of cells after delivery would aid both the research and development of CBT and also its clinical implementation. MRI-based cell tracking, employing magnetically labeled cells has been used for the past 20 years to enable detection of transplanted cells, achieving detection limits of individual cells, in vivo.These individual cells can be detected as dark spots in T2* weighted MRI. Manual enumeration of these spots, and hence, counting cells, in an in vivo MRI is a tedious and highly time consuming task that is prone to inconsistency. Therefore, it becomes practically infeasible for an expert to conduct such manual enumeration for a very large scale analysis, consequentially affecting our ability to monitor CBT. To solve this challenge, we have designed a machine learning methodology for automatically quantifying transplanted cells in MRI in an accurate and efficient manner.

Introduction

Cell based therapy (CBT) is promising for treating a number of diseases (1)(2)(3). The ability to serially and non-invasively measure the number and determine the precise location of cells after delivery would aid both the research and development of CBT and also its clinical implementation. MRI-based cell tracking, employing magnetically labeled cells has been used for the past 20 years to enable detection of transplanted cells, achieving detection limits of individual cells, in vivo (4). These individual cells can be detected as dark spots in T2* weighted MRI (see Fig.1). Manual enumeration of these spots, and hence, counting cells, in an in vivo MRI is a tedious and highly time consuming task that is prone to inconsistency. Therefore, it becomes practically infeasible for an expert to conduct such manual enumeration for a very large scale analysis, consequentially affecting our ability to monitor CBT. To solve this challenge, we have designed a machine learning methodology for automatically quantifying transplanted cells in MRI in an accurate and efficient manner. This machine learning methodology has three key innovations: 1) a convolutional neural network (CNN) is designed for automatic feature extraction; 2) a new transfer learning approach is designed to learn with sparse data; and 3) labeling behavior is exploited in a CNN architecture.

Methods

MRI and Datasets:

A total of 33 in vitro MRI scans were performed using a FLASH sequence with a diverse set of MRI parameters on agarose phantoms that were constructed with a dilute, known number of 4.5 micron diameter MPIOs from Dynal. Each bead has 10 pg of iron, simulating a labeled cell. In vivo MRI of 7 rat brains was performed similarly. 5 rats were previously injected intra-cardiac with MPIO-labeled MSCs, delivering cells to the brain – 2 rats were naïve. (see Fig.1 for more details).

Machine Learning Methods:

A deep learning CNN architecture was designed for detecting transplanted magnetically labeled cells in MRI (see Fig. 2). Deep learning CNN first requires labeled data to learn the concept of a spot in MRI. Once learned, spots can be detected accurately in any unseen MRI and large scale quantification can be performed efficiently. A customized labeling software was designed and developed for an expert to provide training data on spots in MRI using manual 'mouse clicks' (see Fig. 3).

Generally, deep learning requires an enormous amount of labeled data for training, often challenging to obtain in medical applications. Humans, on the other hand, can learn a new visual entity using only few examples, utilizing previously learned concepts. Inspired from this, we re-design the approach to exploit the concept of transfer learning (5). This allows our deep learning approach to first learn about a large number of real world entities such as apples, starts, eyes, etc. and then while learning the concept of spots in MRI utilizes this previous knowledge (see Fig. 4). However, not all the previous knowledge is useful, some can also degrade learning. Therefore, we propose an information theoretic framework to automatically evaluate previous concepts ( previous CNNs) to chose the best source of knowledge prior to conducting transfer. This guarantees the maximum performance increase despite using very small training data.

Moreover, in addition to MRI and labels as training input data, we are the first to demonstrate that the labeling behavior of the medical expert using the software can also be utilized to further improve the performance. The deep learning architecture was further re-designed to exploit labeling behavior as a one-class side information (see Fig.4).

Results and Discussion

Transplanted magnetically labeled cells appeared as dark contrast spots in MRI throughout the brain of rat. The cells were confirmed using histology (See Fig. 1). Area under the ROC (receiving operating curve) was utilized as a standard measure of accuracy. Separate MRI scans were used for training and testing stages. Experimental results show that deep learning can automatically detect cells in vivo with an accuracy of up to 97.3%. A thorough evaluation was performed using multiple in vivo scans using different machines, in vitro scans with known cell numbers, noise added MRI scans etc. Deep learning shows robustness to noise and data variations (see Fig.5).

We demonstrate, using the proposed transfer learning approach, labeling effort of a medical expert can be reduced by 95%, while still maintaining ~90% accuracy. This can potentially impacting a number of clinical applications where obtaining labeled training data is a challenge.

We present a framework to demonstrate that labeling behavior as additional side-information can also be utilized to further improve the performance, on average, by 1%.

Acknowledgements

References

(1) Clinical trial: A Study to Evaluate the Safety of Neural Stem Cells in Patients WithParkinson's Disease, Website https://clinicaltrials.gov/ct2/show/NCT02452723 ( Firstreceived: May 18, 2015 Last updated: March 10, 2016 Last veried: February 2016).

(2) Clinical trial: Umbilical Cord Tissue-derived Mesenchymal Stem Cells for RheumatoidArthritis, Website https://clinicaltrials.gov/ct2/show/NCT01985464 ( First received:October 31, 2013 Last updated: February 4, 2016 Last veried: February 2016).

(3) Clinical trial: Evaluation of Autologous Mesenchymal Stem CellTransplantation (Eects and Side Eects) in Multiple Sclerosis, Web-site https://clinicaltrials.gov/ct2/show/NCT01377870 (First received: June 19,2011 Last updated: April 24, 2014 Last veried: August 2010).

(4) Shapiro, E. M., Sharer, K., Skrtic, S., and Koretsky, A. P. In vivo detection of singlecells by MRI. Magnetic Resonance in Medicine, 2006, 55(2), 242-249.

(5) Pan, S.J. and Yang, Q., 2010. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), pp.1345-1359.