O-260 Identifying seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE) using deep learning
Authors: Elenkov A 
Published: July 2024

Abstract

Study question
Is it possible to utilize artificial intelligence (AI) in the selection of seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE)?

Summary answer
AI algorithm was able to segment seminiferous tubules deemed to be good candidates for sperm retrieval with IoU of 0.746 and F1 score of 72.5%.

What is known already
M-TESE is an advanced surgical procedure reserved for sperm retrieval in men with non-obstructive azoospermia (NOA). Meta-analysis have shown M-TESE might be superior in relation to sperm retrieval rates and postsurgical complications when compared to conventional techniques. Limitations of the technique are the relatively long learning curve for the surgeons and the longer intraoperative time required to identify the tubules with active spermatogenesis. Convolutional neural networks (CNNs) have been shown to successfully identify areas of interest in medical images such as CT and MRI scans. The possibility of utilizing trained CNNs in M-TESE procedures has so far not been studied.

Study design, size, duration
This is a cross sectional study analyzing image data gathered during M-TESE procedures including the first five men enrolled in a prospective study designed specifically to test the utilization of AI in the treatment of NOA. All participants signed written informed consent. Images were collected intraoperatively with low and high resolution cameras according to predefined protocol.

Participants/materials, setting, methods
CNN model with down and upsampling architecture was trained using patch-based technique with corresponding labelled truth image pairs based on combination of semi-automated hue saturation value thresholding and manual segmentation of seminiferous tubules. Tubules were allocated into two different classes by the operating surgeon according to the visual perception of their morphology as type 1 (i.e. larger, opaque, good candidates for micro dissection) and type 2 (i.e. sclerotic or atrophic; unlikely to harbor active spermatogenesis).

Main results and the role of chance
CNN models were trained on 1809 patches with corresponding ground truth segmented images. Two algorithms were tested: the first was trained solely to detect and semantically distinguish type 1 seminiferous tubules. The second was trained to semantically segment the major anatomical structures: tunica albuginea, testicular parenchyma, blood vessels; lobules and the two types of seminiferous tubules describe above. Validation was performed on 988 image patches not previously shown to the algorithm. After grid search for optimal learning rate and augmentation the first algorithm was able to segment type 1 tubules with F1 score of 72.5% and Jaccard Index (IoU) of 0.746. Training took 856 minutes on 48 core high performance computer (HPC) equipped with single graphics processing unit (GPU). Same computational environment was used in the training of the second algorithm. This model was able to segment anatomical structures such as parenchyma (IoU=0.732) and testicular lobules (IoU=0.683) but performed sub optimally on the clinically relevant type 1 tubules (IoU=0.432). The first trained algorithm was able to segment a single high resolution surgical image in 3.4 seconds on an HPC. However, the same procedure took 5.5 minutes on standard desktop computer with 8 core Intel processor without GPU.

Limitations, reasons for caution
The number of patients is relatively small and therefore more training data would be needed to achieve robust performance. Training data were labelled by single surgeon limiting the generalizability and possibly biasing the selection process. The need of HPC environment for real time segmentation can limit the clinical use.

Wider implications of the findings
This pilot study shows the potential of AI to help in choosing suitable tubules for micro dissection, shortening the learning curve for young andrological surgeons. The access to cluster based computation is getting easier by the day and can soon allow instant intraoperative AI segmentation, thus shortening the surgical procedure.

Trial registration number
NA