# Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step
Transfer Learning

Francisco Lopez-Tiro1,2,3, Juan Pablo Betancur-Rengifo3, Arturo Ruiz-Sanchez3,
Ivan Reyes-Amezcua3,4,
Jonathan El-Beze5, Jacques Hubert5, Michel Daudon6, Gilberto Ochoa-Ruiz*,1,3,
Christian Daul*,2
1Tecnologico de Monterrey, School of Sciences and Engineering, Mexico2CRAN
(UMR 7039, Université de Lorraine and CNRS), Nancy, France3CV-INSIDE Lab
Member, Mexico4CINVESTAV, Computer Sciences Department, Mexico5CHU Nancy,
Service d’urologie de Brabois, Vandœuvre-lès-Nancy, France 6Hôpital Tenon, AP-
HP, Paris, France*Corresponding<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Knowing the cause of kidney stone formation is crucial to establish treatments
that prevent recurrence. There are currently different approaches for
determining the kidney stone type. However, the reference ex-vivo
identification procedure can take up to several weeks, while an in-vivo visual
recognition requires highly trained specialists. Machine learning models have
been developed to provide urologists with an automated classification of
kidney stones during an ureteroscopy; however, there is a general lack in
terms of quality of the training data and methods. In this work, a two-step
transfer learning approach is used to train the kidney stone classifier. The
proposed approach transfers knowledge learned on a set of images of kidney
stones acquired with a CCD camera (ex-vivo dataset) to a final model that
classifies images from endoscopic images (ex-vivo dataset). The results show
that learning features from different domains with similar information helps
to improve the performance of a model that performs classification in real
conditions (for instance, uncontrolled lighting conditions and blur). Finally,
in comparison to models that are trained from scratch or by initializing
ImageNet weights, the obtained results suggest that the two-step approach
extracts features improving the identification of kidney stones in endoscopic
images.

###### Index Terms:

Transfer learning, kidney stones, deep learning

##  I Introduction

The formation of kidney stones in the urinary tract is a public health issue
[1, 2]. In industrialized countries, 10% of the population suffers from an
episode of kidney stones during their lifetime. Recent studies have determined
that the risk of recurrence increases up to $40\%$ in less than 5 years [3,
4]. Thus, determining the root cause of kidney stone formation is crucial to
avoid relapses through personalized treatments [5, 3, 6]. Therefore, different
approaches for visually identifying some of the most common types (or classes)
of kidney stones have been proposed in recent years [7, 8].

The Morpho-Constitutional Analysis (MCA) is currently the reference method for
the identification of the type of the extracted kidney stone fragments [9].
This ex-vivo procedure consists of two complementary analyses on the extracted
kidney stone parts, which were fragmented with a laser. The fragments are
visually inspected under a microscope to observe the colors and textures of
their surface and section. Then, an infrared-spectrophotometry analysis
enables to identify the molecular and crystalline composition of the different
areas (layers) of the kidney stone [10]. However, in numerous hospitals the
MCA results are only available after some weeks. This delay makes it difficult
to establish an immediate and appropriate treatment for the patient. On the
other hand, removing large kidney stone fragments is often difficult in
practice. Moreover, the biochemical composition can be altered by the laser
during the fragmentation [11], making the MCA procedure challenging in some
cases.

Endoscopic Stone Recognition (ESR) is a promising technique to immediately
determine the type of kidney stones during the ureteroscopy (i.e., in-vivo
recognition). The advantage of ESR is twice: kidney stones can be pulverized
(dusting procedure with a laser) instead fragmented, and an appropriate
treatment can be immediately defined. ESR is only based on a visual inspection
of in-vivo endoscopic images observed on a screen. For trained urologists, ESR
results are strongly correlated with those of MCA [12]. However, only a few
highly trained experts are nowadays able to recognize the type of kidney
stones using only endoscopic images. Moreover, the visual classification by
urologists is operator dependent and subjective, and the required experience
is long to acquire [13].

Studies have been recently proposed to automate ESR [14, 12, 15]. These Deep
Learning (DL) based methods led to promising results. However, one of the most
common challenges in these DL-based methods for classifying kidney stones is
the lack of a large image set for the model training. In addition, the
similarity of the data distribution is another important factor to obtain an
adequate model. Consequently, this suggests a trade-off between the amount of
available data and the data distribution to fit the network weights
adequately. The majority of the DL-based models report fine-tuning of weights
learned from distributions other than those from kidney stone images (commonly
from ImageNet [16]).

(a) Dataset A: CCD-camera images (ex-vivo)

(b) Dataset B: Endoscopic images (ex-vivo)

Figure 1: Examples of ex-vivo kidney stone images acquired with (a) a CCD
camera [9] and (b) an endoscope [18]. SEC and SUR stand for section and
surface views, respectively. The class types (WW, STR, CYS, etc.) are defined
in Table I.

Transfer Learning (TL) is used when features learned from a given domain (or
class of images) can bring appropriate knowledge to another domain for which
the available image set is too small to train a large model from scratch [22,
17]. In the context of ureteroscopy, a large dataset of in-vivo images is
currently not available and collecting such a large database of endoscopic
images during ureteroscopies is a long term work. However, in the context of
this work, images of ex-vivo kidney stone fragments (acquired with standard
CCD cameras) are available. Due to their similarity in color, texture and
morphological features, TL can be used to distill knowledge from CCD-camera
images into the final classifier of the images acquired with endoscopes.

Based on this idea, a two-step TL model to classify six types of kidney stones
is proposed. The model uses a homogeneous, as well as a heterogeneous TL phase
on a ResNet50 architecture pre-trained with the ImageNet dataset. To validate
our proposal, the approach transfers knowledge learned on a small set of
images acquired with classical CCD cameras to a final model that classifies
ex-vivo endoscopic images.

The rest of this paper is organized as follows. Section II provides an
overview of the ex-vivo datasets, namely the CCD (digital) camera image and
endoscopic image sets. Section II also presents the two-step TL approach.
Section III compares the performances of the two-step TL approach with those
of the methods of the literature. Finally, Section IV concludes this
contribution and proposes perspectives.

##  II Materials and Methods

###  II-A Datasets

Two kidney stone datasets were used in our experiments (see Table I): images
were acquired with standard CCD cameras, and endoscopic images were captured
with an ureteroscope. The dataset’s main characteristics are described below.

Dataset A, [9]. This ex-vivo dataset of 366 CCD camera images (see the two
upper lines in Fig. 1a) is split in 209 surface and 157 section images, and
contains six different stone types sorted by sub-types denoted by WW
(Whewellite, sub-type Ia), CAR (Carbapatite, IVa), CAR2 (Carbapatite, IVa2),
STR (Struvite, IVc), BRU (Brushite, IVd) and CYS (Cystine, Va). The fragment
images were acquired with a digital camera under controlled lighting
conditions and with a uniform background.

Dataset B, [18]. The endoscopic dataset consists of 409 images (see the two
upper lines in Fig. 1b). This dataset includes 246 surface and section 163
images. Dataset B involves the same classes as dataset A, except that the
carbatite stones (sub-types IVa1 and IVa2) are replaced by the weddelite (sub-
type IIa) and uric acid (IIIa) classes. The images of dataset B were captured
with an endoscope by placing kidney stone fragments in an environment
simulating in a realistic way the shape and color or ureters (for more details
see [18]). These images are visually close to in-vivo images since the
fragments were acquired with an ureteroscope and by simulating a quite
realistic the clinical in-vivo scenes.

TABLE I: Description of the two ex-vivo datasets. Dataset A (M. Corrales et
al. [9])
---
Subtype | Main component | Key | Surface | Section | Mixed
Ia | Whewellite | WW | 50 | 74 | 124
IVa1 | Carbapatite | CAR | 18 | 18 | 36
IVa2 | Carbapatite | CAR2 | 36 | 18 | 54
IVc | Struvite | STR | 25 | 19 | 44
IVd | Brushite | BRU | 43 | 17 | 60
Va | Cystine | CYS | 37 | 11 | 48
|  | TOTAL | 209 | 157 | 366
Dataset B (J. El-Beze et al. [18])
Subtype | Main component | Key | Surface | Section | Mixed
Ia | Whewellite | WW | 62 | 25 | 87
IIa | Weddellite | WD | 13 | 12 | 25
IIIa | Acide Urique | AU | 58 | 50 | 108
IVc | Struvite | STR | 43 | 24 | 67
IVd | Brushite | BRU | 23 | 4 | 27
Va | Cystine | CYS | 47 | 48 | 95
|  | TOTAL | 246 | 163 | 409

Due to the small size of the two datasets, and due to their class imbalance,
patches were extracted from the images to increase and balance the number of
training and testing samples. The two last lines of Figs. 1a and 1b show such
patches. As demonstrated in previous works [15, 19, 20], the use of square
patches of appropriate size allows to capture sufficient color and texture
information for the classification. In addition, the use of patches instead of
full surface and section images allows for augmenting and balancing the
datasets. According to [20], a patch size of $256\times 256$ pixels was chosen
for the A and B datasets. A patch overlap of at most 20 pixels is set to avoid
redundant information inside the image of a same kidney stone fragment. A
total of 12,000 patches were extracted, both for dataset A and dataset B.

The patches of each dataset were “whitened” using the mean $m_{i}$ and
standard deviation $\sigma_{i}$ of the color values $I_{i}^{w}$ for each RGB
channel with $I_{i}^{w}=(I_{i}–m_{i})/\sigma_{i}$, with i = R, G, B. To avoid
data leakage in the datasets, a random, non-repeating dataset partitioning
strategy was used (in contrast to previous works that used repeated images in
both sets). $80\%$ of the data was used for training and $20\%$ for testing.

###  II-B Two-step transfer learning

Figure 2: Two-step TL workflow. Model A was first initialized with the weights
of a ResNet50 network pre-trained with ImageNet, and then fine-tuned with
Dataset A. Next, Model B starts with the weights learned from Model A and is
finally fine-tuned with Dataset B.

Depending on the difference between two image domains, TL can be classified
into homogeneous and heterogeneous transfer techniques. Homogeneous TL (HoTL)
is applicable when specific datasets relating to a particular domain are
available, even if the later is only “similar” to the images of the target
domain (i.e., the dataset from which the knowledge is transferred does not
exactly match the target dataset, but carries similar information). On the
other hand, heterogeneous TL (HeTL) is the case where the datasets of the
source and target domains differ [21, 22]. Furthermore, when a reduced amount
of training data is available, it is recommended to initialize the weights of
architectures with pre-trained values rather than random values (i.e., TL from
the scratch) [22]. Thus, in the first step of the proposed two-step strategy,
a large dataset (ImageNet) is used to transfer knowledge into a network
(ResNet50) which is fine-tuned by the smaller kidney stone image set acquired
under controlled acquisition conditions (dataset A). After this HeTL step, an
HoTL is used, this second step exploiting dataset B including endoscopic
images close to dataset A, but with more image quality variability as really
encountered in ureteroscopy.

First TL step: HeTL. Figure 2 shows the workflow of the two-step TL process.
The first step is the heterogeneous phase in which the weights are
initialized. To do so, a ResNet50 architecture was pre-trained with ImageNet
[16] and used to train a model able to classify the six types of kidney stones
from dataset A (see Table I).

In this HeTL step, Gaussian blur and geometrical transformations are only
applied to the training images with the aim of preparing the model for dataset
B. A batch size of 24 was used along with a SGD optimizer with a learning rate
of 0.001 and momentum of 0.9. Fully connected layers with 768,256,128 and 6
neurons were added with batch-normalization, ReLU activation function, and a
dropout of 0.5.

Second TL-step: HoTL. The homogeneous learning occurs in the second step of
the TL-process. It consists in transferring the knowledge (weights) of the
trained model from the HeTL into dataset B to differentiate between the six
types of kidney stones that are in this dataset (see Table I). The initial
weights of model B are those after the fine-tuning of model A with dataset A,
model B being finally fine-tuned with dataset B. The purpose of this approach
is to improve the generalization performance of model B and facilitate the
extraction of robust features [23]. Here, only geometric transformations were
applied to the patches, since the image quality variability in dataset B is
naturally high, while this variability is limited in dataset A. Moreover, 30
epochs were also executed with a SGD optimizer, but with a larger learning
rate of 0.01 since it was expected that the model had less to learn. However,
fully connected layers were not added since the idea was to use the trained
model without further modifications to the architecture.

TABLE II: Mean $\pm$ standard deviation determined for each metric quantifying the results for each patch type set (fragment surface patches, section patches, and both patch types mixed) and for various TL-strategies after 5 executions. Accuracy, Precision, Recall, and F1-Score were used to measure over six classes the performance of the models for each TL-strategy. Patch type | TL strategy | Accuracy | Precision | Recall | F1-Score | Dataset | Training details
---|---|---|---|---|---|---|---
Surface | No TL | 0.582$\pm$0.033 | 0.588$\pm$0.028 | 0.582$\pm$0.033 | 0.579$\pm$0.028 | A | Baseline (no TL) trained on dataset A
No TL | 0.702$\pm$0.012 | 0.718$\pm$0.010 | 0.702$\pm$0.012 | 0.701$\pm$0.008 | B | Baseline (no TL) trained on dataset B
HeTL only | 0.649$\pm$0.050 | 0.655$\pm$0.039 | 0.649$\pm$0.050 | 0.642$\pm$0.046 | A | Baseline + TL with ImageNet weights
HeTL only | 0.820$\pm$0.033 | 0.833$\pm$0.029 | 0.820$\pm$0.033 | 0.818$\pm$0.032 | B | Baseline + TL with ImageNet weights
HeTL + HoTL | 0.832$\pm$0.012 | 0.845$\pm$0.012 | 0.832$\pm$0.012 | 0.829$\pm$0.012 | B | Baseline + TL with ImageNet +TL dataset A
Section | No TL | 0.592$\pm$0.039 | 0.627$\pm$0.029 | 0.592$\pm$0.039 | 0.596$\pm$0.039 | A | Baseline (no TL) trained on dataset A
No TL | 0.738$\pm$0.022 | 0.772$\pm$0.015 | 0.738$\pm$0.022 | 0.722$\pm$0.023 | B | Baseline (no TL) trained on dataset B
HeTL only | 0.824$\pm$0.022 | 0.834$\pm$0.020 | 0.824$\pm$0.022 | 0.820$\pm$0.023 | A | Baseline + TL with ImageNet weights
HeTL only | 0.873$\pm$0.041 | 0.897$\pm$0.021 | 0.873$\pm$0.041 | 0.872$\pm$0.043 | B | Baseline + TL with ImageNet weights
HeTL + HoTL | 0.904$\pm$0.048 | 0.915$\pm$0.037 | 0.904$\pm$0.048 | 0.903$\pm$0.050 | B | Baseline + TL with ImageNet +TL dataset A
Mixed | No TL | 0.594$\pm$0.021 | 0.610$\pm$0.023 | 0.594$\pm$0.021 | 0.596$\pm$0.020 | A | Baseline (no TL) trained on dataset A
No TL | 0.760$\pm$0.024 | 0.773$\pm$0.029 | 0.760$\pm$0.024 | 0.752$\pm$0.024 | B | Baseline (no TL) trained on dataset B
HeTL only | 0.800$\pm$0.013 | 0.809$\pm$0.013 | 0.800$\pm$0.013 | 0.797$\pm$0.013 | A | Baseline + TL with ImageNet weights
HeTL only | 0.837$\pm$0.032 | 0.848$\pm$0.030 | 0.837$\pm$0.032 | 0.834$\pm$0.035 | B | Baseline + TL with ImageNet weights
HeTL + HoTL | 0.856$\pm$0.001 | 0.868$\pm$0.002 | 0.856$\pm$0.001 | 0.854$\pm$0.001 | B | Baseline + TL with ImageNet +TL dataset A
Figure 3: UMAP-ICN dimensionality feature reduction [24]. From left to right:
surface, section, and mixed patch sets. The visualizations were generated in
the second step of the “HeTL+HoTL” strategy (see Fig. 2).

##  III Results and discussion

Three different experiments were carried out to assess the performance of the
two-step TL approach presented in Section II-B using patch data described in
Section II-A. The ability of the two-step TL approach (see Fig. 2) to predict
kidney stone types on endoscopic and digital camera images was evaluated with
surface (first experiment) and section patches (second experiment), these
patch types being separately used. In the third experiment, based on a “mixed”
dataset, the performance of the two-step TL approach was evaluated by
simultaneously using surface and section patches. In previous works based on
DL [15, 19, 20] it has been reported that the combination of kidney stone
patch types improves the classification process over models trained with only
one patch type. Furthermore, mixing patch types closely simulates the way
experts perform MCA and ESR [9, 10]. The results of our experiments are
summarized in Table II and discussed below.

###  III-A Two step TL-approach results

Experiment 1. From the surface patch results, it can be seen that the weights
heterogeneously transferred from ResNet50 to model A (the “HeTL only” strategy
applied on dataset A, see Table II) led to an accuracy of 0.649$\pm$0.050) is
useful to avoid training the model from scratch (accuracy of 0.582$\pm$0.033
for the “No TL” strategy applied on dataset A). Although the performance of
“HeTL only” remains low, it is also noticeable that the two-step TL strategy
(“HeTL+HoTL”) of Fig. 2 improves significantly the identification performance,
obtaining an overall accuracy over 6 classes of 0.832$\pm$0.012 (increase of
18% compared the “HeTL only” strategy with dataset A). The accuracy increase
from “HeTL only” to the “HeTL+HoTL” strategy is due to the similarity of the
colors and textures of kidney stone fragments in databases A and B.

Experiment 2. For section patches, the results of “HeTL only” applied on
dataset A are promising (accuracy of 0.824$\pm$0.022). This high performance
was reached due to the rich textural information in section patches which is
not present in surface images. An accuracy of 0.904$\pm$0.048 was obtained for
“HeTL+HoTL” applied on dataset B. Although the 8% increase from “HeTL only” to
the “HeTL+HoTL” strategy was smaller for section data as for surface patches,
this accuracy was the highest one in all three experiments.

Experiment 3. The “HeTL” strategy applied on mixed patches of dataset A, led
to an accuracy of 0.8$\pm$0.013. This performance in increased by 5% by the
“HeTL+HoTL” strategy which has an accuracy of 0.856$\pm$0.001. In previous
contributions it has been reported that the simulatneous use of surface and
section led to the highest performance. This observation is not confirmed
here, since the best results were obtained for section patches in experiment
2.

Table II shows that, in comparison to a learning from scratch, all TL-
strategies improve the values of all four performance criteria, whatever the
dataset. The UMAP-ICN visualisation [24] of Fig. 3 represents the features
extracted in the last step of the “HeTL+HoTL” strategy. It is visible that for
all patch types, the inter-class distance is high and the intra-class distance
is weak.

###  III-B Comparison with the state-of-the-art

Table III details the performance of reference DL-based methods used to
identify the type of kidney stones using endoscopic image patches. These
methods were [12, 14, 19] were all implemented and evaluated on dataset B
(endoscopic dataset), and compared to the TL-approach described in this
contribution. The results demonstrate that the two-step TL model outperforms
the solutions described in [12, 14, 19] in terms of accuracy. Thus, DL-
strategies involving a pre-training with a general database, followed by a
first tuning with a specific database, and ending with a final tuning with the
target database can effectively lead to an improved performance on different
data distributions without the need of a large amount of data.

TABLE III: Comparison of the performance of various kidney stone identification methods. The value of the accuracy over all classes was determined with dataset B for all methods. Method | Surface | Section | Mixed
---|---|---|---
Martinez, et al. [19] | 0.562$\pm$0.233 | 0.466$\pm$0.128 | 0.527$\pm$0.189
Black, et al. [14] | 0.735$\pm$0.190 | 0.762$\pm$0.185 | 0.801$\pm$0.138
Estrade, et al. [12] | 0.737$\pm$0.179 | 0.788$\pm$0.106 | 0.701$\pm$0.223
This contribution | 0.832$\pm$0.012 | 0.904$\pm$0.048 | 0.856$\pm$0.001

##  IV Conclusion and perspective

It was demonstrated that it is possible to classify six different types of
kidney stones using a small datasets of endoscopic images, the strategy being
first to pre-learn the model with images acquired under controlled acquisition
conditions (CCD camera) and then to exploit a fine tuning of the model using
images captured in conditions simulating in a realistic way an ureteroscopy.
This study confirms that it is easier for a neural network to adjust the
weights learned on similar distributions and adapt them to a multiple class
task. It is desirable that models of this type should be adapted to identify
kidney stones using the complete endoscopic images instead of patches. We
believe that the proposed approach will facilitate training on whole-image
models when the datasets are reduced in the number of images.

## Acknowledgments

The authors wish to thank the AI Hub and the CIIOT at Tecnologico de Monterrey
for their support for carrying the experiments reported in this paper in their
NVIDIA’s DGX computer.

We also wish thank the Verano de la Investigación Cientifica (VICI) Delfin
program for assisting Juan Pablo Betancur-Rengifo with a mobility grant, and
CONACYT for the doctoral scholarship for Francisco Lopez-Tiro at Tecnologico
de Monterrey, and Ivan Reyes-Amezcua at CINVESTAV.

## Compliance with ethical approval

The images were captured in medical procedures following the ethical
principles outlined in the Helsinki Declaration of 1975, as revised in 2000,
with the consent of the patients.

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